© Springer Nature Switzerland AG 2020. Security specialists have been developing and implementing many countermeasures against security threats, which is needed because the number of new security threats is further and further growing. In this chapter, we introduce an approach for identifying hidden security threats by using Uniform Resource Locators (URLs) as an example dataset, with a method that automatically detects malicious URLs by leveraging machine learning techniques. We demonstrate the effectiveness of the method through performance evaluations.

Since the 1980s, domain names and the domain name system (DNS) have been used and abused. Although legitimate Internet users rely on domain names as indispensable infrastructures for using the Internet, attackers use or abuse them as reliable, instantaneous, and distributed attack infrastructures. However, there is a lack of complete understanding of such domain-name abuses and methods for coping with them. In this study, we designed and implemented a unified analysis system combining current defense solutions to build actionable threat intelligence from malicious domain names. The basic concept underlying our system is malicious domain name chromatography. Our analysis system can distinguish among mixtures of malicious domain names for websites. On the basis of this concept, we do not create a hodgepodge of current solutions but design separation of abused domain names and offer actionable threat intelligence or defense information by considering the characteristics of malicious domain names as well as the possible defense solutions and points of defense. Finally, we evaluated our analysis system and defense-information output using a large real dataset to show the effectiveness and validity of our system.

Damage caused by malware has become a serious problem. The recent rise in the spread of evasive malware has made it difficult to detect it at the pre-infection timing. Malware detection at post-infection timing is a promising approach that fulfills this gap. Given this background, this work aims to identify likely malware-infected devices from the measurement of Internet traffic. The advantage of the traffic-measurementbased approach is that it enables us to monitor a large number of endhosts. If we find an endhost as a source of malicious traffic, the endhost is likely a malware-infected device. Since the majority of malware today makes use of the web as a means to communicate with the C&amp
C servers that reside on the external network, we leverage information recorded in the HTTP headers to discriminate between malicious and benign traffic. To make our approach scalable and robust, we develop the automatic template generation scheme that drastically reduces the amount of information to be kept while achieving the high accuracy of classification
since it does not make use of any domain knowledge, the approach should be robust against changes of malware. We apply several classifiers, which include machine learning algorithms, to the extracted templates and classify traffic into two categories: malicious and benign. Our extensive experiments demonstrate that our approach discriminates between malicious and benign traffic with up to 97.1% precision while maintaining the false positive rate below 1.0%.

Mobile app stores, such as Google Play, play a vital role in the ecosystem of mobile device software distribution platforms. When users find an app of interest, they can acquire useful data from the app store to inform their decision regarding whether to install the app. This data includes ratings, reviews, number of installs, and the category of the app. The ratings and reviews are the user-generated content (UGC) that affect the reputation of an app. Therefore, miscreants can leverage such channels to conduct promotional attacks
for example, a miscreant may promote a malicious app by endowing it with a good reputation via fake ratings and reviews to encourage would-be victims to install the app. In this study, we have developed a system called PADetective that detects miscreants who are likely to be conducting promotional attacks. Using a 1723-entry labeled dataset, we demonstrate that the true positive rate of detection model is 90%, with a false positive rate of 5.8%. We then applied our system to an unlabeled dataset of 57M reviews written by 20M users for 1M apps to characterize the prevalence of threats in the wild. The PADetective system detected 289K reviewers as potential PA attackers. The detected potential PA attackers posted reviews to 136K apps, which included 21K malicious apps. We also report that our system can be used to identify potentially malicious apps that have not been detected by anti-virus checkers.

Domain names are at the base of today’s cyber-attacks. Attackers abuse the domain name system (DNS) to mystify their attack ecosystems
they systematically generate a huge volume of distinct domain names to make it infeasible for blacklisting approaches to keep up with newly generated malicious domain names. To solve this problem, we propose DomainProfiler for discovering malicious domain names that are likely to be abused in future. The key idea with our system is to exploit temporal variation patterns (TVPs) of domain names. The TVPs of domain names include information about how and when a domain name has been listed in legitimate/popular and/or malicious domain name lists. On the basis of this idea, our system actively collects historical DNS logs, analyzes their TVPs, and predicts whether a given domain name will be used for malicious purposes. Our evaluation revealed that DomainProfiler can predict malicious domain names 220 days beforehand with a true positive rate of 0.985. Moreover, we verified the effectiveness of our system in terms of the benefits from our TVPs and defense against cyber-attacks.

Network equipment, such as routers, switches, and RA- DIUS servers, generate various log messages induced by network events such as hardware failures and protocol flaps. In large production networks, analyzing the log messages is crucial for diagnosing network anomalies
however, it has become challenging due to the following two reasons. First, the log messages are composed of unstructured text messages generated in accordance with vendor-specific rules. Second, network events that in- duce the log messages span several geographical locations, network layers, protocols, and services. We developed a method to tackle these obsta- cles consisting of two techniques: statistical template extraction (STE) and log tensor factorization (LTF). The former leverages a statistical clustering technique to automatically extract primary templates from unstructured log messages. The latter builds a statistical model that collects spatial-Temporal patterns of log messages. Such spatial-Temporal patterns provide useful in- sights into understanding the impact and patterns of hidden network events. We evaluate our techniques using a massive amount of network log mes- sages collected from a large operating network and confirm that our model fits the data well. We also investigate several case studies that validate the usefulness of our method.

There are many studies for recognizing eating moments using wide types of modality (e.g. arm motion). However, they are needed to be improved for both accuracy and robustness for practical use in daily life. In this paper, we propose a novel recognition method using bimodal heart rate responses caused by eating. Our method combines (i) short-term and (ii) long-term features of heart rate changes. The proposed method was evaluated for recognizing eating moment with the free-environment dataset (9 participants, 604 days), and achieved 98.6% accuracy and 56.9% F-score. The proposed features related to ingestion and digestion contribute to robust eating moment recognition.

Domain names and domain name system (DNS) have been used and abused for over 30 years since the 1980s. Although legitimate Internet users rely on domain names as their indispensable infrastructures for using the Internet, attackers use or abuse them as reliable, instantaneous, and distributed attack infrastructure. However, there is a lack of complete understanding of such domain name abuses and the methods for coping with them. In this paper, we design and implement a unified and objective analysis pipeline combining the existing defense solutions to realize practical and optimal defenses against today's malicious domain names. The basic concept underlying our novel analytical approach is malicious domain names' chromatography. Our new analysis pipeline can distinguish among mixtures of malicious domain names for websites. On the basis of this concept, we do not create a hodgepodge of existing solutions but design separation of abused domain names and offer defense information by considering the characteristics of malicious domain names as well as the possible defense solutions and points of defense. Finally, we evaluate our analysis pipeline and output defense information using a large and real dataset to show the effectiveness and validity of our proposed approach.

This work develops a method of detecting and classifying 'potentially unwanted applications' (PUAs) such as adware or remote monitoring tools. Our approach leverages DNS queries made by apps. Using a large sample of Android apps from third-party marketplaces, we first reveal that DNS queries can provide useful information for the detection and classification of PUAs. Next, we show that existing DNS blacklists are ineffective to perform these tasks. Finally, we demonstrate that our methodology performed with high accuracy.

Android is one of the most popular mobile device platforms. However, since Android apps can be disassembled easily, attackers inject additional advertisements or malicious codes to the original apps and redistribute them. There are a non-negligible number of such repackaged apps. We generally call those malicious repackaged apps "clones." However, there are apps that are not clones but are similar to each other. We call such apps "relatives." In this work, we developed a framework called APPraiser that extracts similar apps and classifies them into clones and relatives from the large dataset. We used the APPraiser framework to study over 1.3 million apps collected from both official and third-party marketplaces. Our extensive analysis revealed the following findings: In the official marketplace, 79% of similar apps were attributed to relatives, while in the third-party marketplace, 50% of similar apps were attributed to clones. The majority of relatives are apps developed by prolific developers in both marketplaces. We also found that in the third-party market, of the clones that were originally published in the official market, 76% of them are malware.

An enormous number of malware samples pose a major threat to our networked society. Antivirus software and intrusion detection systems are widely implemented on the hosts and networks as fundamental countermeasures. However, they may fail to detect evasive malware. Thus, setting a high priority for new varieties of malware is necessary to conduct in-depth analyses and take preventive measures. In this paper, we present a traffic model for malware that can classify network behaviors of malware and identify new varieties of malware. Our model comprises malwarespecific features and general traffic features that are extracted from packet traces obtained from a dynamic analysis of the malware. We apply a clustering analysis to generate a classifier and evaluate our proposed model using large-scale live malware samples. The results of our experiment demonstrate the effectiveness of our model in finding new varieties of malware.

We developed a novel, proof-of-concept side-channel attack framework called RouteDetector, which identifies a route for a train trip by simply reading smart device sensors: an accelerometer, magnetometer, and gyroscope. All these sensors are commonly used by many apps without requiring any permissions. The key technical components of RouteDetector can be summarized as follows. First, by applying a machine-learning technique to the data collected from sensors, RouteDetector detects the activity of a user, i.e., "walking," "in moving vehicle," or " other. "Next, it extracts departure/arrival times of vehicles from the sequence of the detected human activities. Finally, by correlating the detected departure/ arrival times of the vehicle with timetables/route maps collected from all the railway companies in the rider's country, it identifies potential routes that can be used for a trip. We demonstrate that the strategy is feasible through field experiments and extensive simulation experiments using timetables and route maps for 9,090 railway stations of 172 railway companies.

Web tracking is widely used as a means to track user's behavior on websites. While web tracking provides new opportunities of e-commerce, it also includes certain risks such as privacy infringement. Therefore, analyzing such risks in the wild Internet is meaningful to make the user's privacy transparent. This work aims to understand how the web tracking has been adopted to prominent websites. We also aim to understand their resilience to the ad-blocking techniques. Web tracking-enabled websites collect the information called the web browser fingerprints, which can be used to identify users. We develop a scalable system that can detect fingerprinting by using both dynamic and static analyses. If a tracking site makes use of many and strong fingerprints, the site is likely resilient to the ad-blocking techniques. We also analyze the connectivity of the third-party tracking sites, which are linked from multiple websites. The link analysis allows us to extract the group of associated tracking sites and understand how influential these sites are. Based on the analyses of 100,000 websites, we quantify the potential risks of the web tracking-enabled websites. We reveal that there are 226 websites that adopt fingerprints that cannot be detected with the most of off-the-shelf anti-tracking tools. We also reveal that a major, resilient third-party tracking site is linked to 50.0 % of the top-100,000 popular websites.

Today, websites are exposed to various threats that exploit their vulnerabilities. A compromised website will be used as a stepping-stone and will serve attackers' evil purposes. For instance, URL redirection mechanisms have been widely used as a means to perform web based attacks covertly; i.e., an attacker injects a redirect code into a compromised website so that a victim who visits the site will be automatically navigated to a malware distribution site. Although many defense operations against malicious websites have been developed, we still encounter many active malicious websites today. As we will show in the paper, we infer that the reason is associated with the evolution of the ecosystem of malicious redirection.
Given this background, we aim to understand the evolution of the ecosystem through long-term measurement. To this end, we developed a honeypot-based monitoring system, which specializes in monitoring the behavior of URL redirections. We deployed the monitoring system across four years and collected more than 100K malicious redirect URLs, which were extracted from 776 distinct websites. Our chief findings can be summarized as follows: (1) Click-fraud has become another motivation for attackers to employ URL redirection, (2) The use of web-based domain generation algorithms (DGAs) has become popular as a means to increase the entropy of redirect URLs to thwart URL blacklisting, and (3) Both domain flux and IP-flux are concurrently used for deploying the intermediate sites of redirect chains to ensure robustness of redirection.
Based on the results, we also present practical countermeasures against malicious URL redirections. Security/network operators can leverage useful information obtained from the honeypot-based monitoring system. For instance, they can disrupt infrastructures of web based attack by taking down domain names extracted from the monitoring system. They can also collect web advertising/tracking IDs, which can be used to identify the criminals behind attacks. (C) 2017 The Author(s). Published by Elsevier Ltd.

Damage caused by malware is a serious problem that needs to be addressed. The recent rise in the spread of evasive malware has made it difficult to detect it at the pre-infection timing. Malware detection at post-infection timing is a promising approach that fulfills this gap. Given this background, this work aims to identify likely malware-infected devices from the measurement of Internet traffic. The advantage of the traffic-measurement-based approach is that it enables us to monitor a large number of clients. If we find a client as a source of malicious traffic, the client is likely a malware-infected device. Since the majority of malware today makes use of the web as a means to communicate with the C&amp
C servers that reside on the external network, we leverage information recorded in the HTTP headers to discriminate between malicious and legitimate traffic. To make our approach scalable and robust, we develop the automatic template generation scheme that drastically reduces the amount of information to be kept while achieving the high accuracy of classification
since it does not make use of any domain knowledge, the approach should be robust against changes of malware. We apply several classifiers, which include machine learning algorithms, to the extracted templates and classify traffic into two categories: malicious and legitimate. Our extensive experiments demonstrate that our approach discriminates between malicious and legitimate traffic with up to 97.1% precision while maintaining the false positive below 1.0%.

This paper reports a large-scale study that aims to understand how mobile application (app) vulnerabilities are associated with software libraries. We analyze both free and paid apps. Studying paid apps was quite meaningful because it helped us understand how differences in app development/maintenance affect the vulnerabilities associated with libraries. We analyzed 30k free and paid apps collected from the official Android marketplace. Our extensive analyses revealed that approximately 70%/50% of vulnerabilities of free/paid apps stem from software libraries, particularly from third-party libraries. Somewhat paradoxically, we found that more expensive/popular paid apps tend to have more vulnerabilities. This comes from the fact that more expensive/popular paid apps tend to have more functionality, i.e., more code and libraries, which increases the probability of vulnerabilities. Based on our findings, we provide suggestions to stakeholders of mobile app distribution ecosystems.

<p>Hardware Trojans (HT) that are implemented at the time of manufacturing ICs are being reported as a new threat that could destroy the IC or degrade its security under specific circumstances, and is becoming a key security challenge that must be addressed. On the other hand, since it is also common to use components manufactured or bought via third parties in portions outside of the substrate on which the IC is mounted or communication lines connecting the IC and the substrate, there is a possibility that HTs may also be set in the peripheral circuits of the IC in the same manner as in the IC. In this paper, we developed an HT that could be implemented in the peripheral circuits and wiring of an IC, investigated the possibility of being able to acquire information processed inside a device by measuring the electromagnetic waves generated and leaked by Intentional Electromagnetic Interference (IEMI) with HT outside the device, and investigated detection methods for cases where such HTs are implemented.</p>

This paper investigates fundamental mechanisms of brightness changes in heart rate (HR) measurement from face images through three kinds of experiments
(i) measurement of light reflection from cheek covered with/without copper film, (ii) spectroscopy measurement of reflection light from face and (iii) simultaneous measurement of face images and laser speckle images. The brightness change of the face skin are found to be caused by both the green light absorption variation by the blood volume changes and the light reflection variation by pulsatory face movements. The Real-time Pulse Extraction Method (RPEM), designed to extract the variation of light absorption by removing motion noise, is corroborated for the robustness by comparing the RPEM with the pulse wave of the ear photoplethysmography. The RPEM is also applied to heart rate measurements of seven participants during office work under non-controlled condition in order to evaluate continuous real-time HR monitoring. RMSE = 6.7 bpm is achieved as an average result of seven participants in five days with the 44% of HR measured rate with respect to the number of reference HRs from the electrocardiogram during face is detected. The result indicates that the RPEM method enables HR monitoring in daily life.

Mobile app stores, such as Google Play, play a vital role in the ecosystem of mobile apps. When users look for an app of interest, they can acquire useful data from the app store to facilitate their decision on installing the app or not. This data includes ratings, reviews, number of installs, and the category of the app. The ratings and reviews are the user-generated content (UGC) that affect the reputation of an app. Unfortunately, miscreants also exploit such channels to conduct promotional attacks (PAs) that lure victims to install malicious apps. In this paper, we propose and develop a new system called PADetective to detect miscreants who are likely to be conducting promotional attacks. Using a dataset with 1,723 of labeled samples, we demonstrate that the true positive rate of detection model is 90%, with a false positive rate of 5.8%. We then applied PADetective to a large dataset for characterizing the prevalence of PAs in the wild and find 289 K potential PA attackers who posted reviews to 21 K malicious apps.

Adoption of SSL/TLS to protect the privacy of web users has become increasingly common. In fact, as of September 2015, more than 68% of top-1M websites deploy SSL/TLS to encrypt their traffic. The transition from HTTP to HTTPS has brought a new challenge for network operators who need to understand the hostnames of encrypted web traffic for various reasons. To meet the challenge, this work develops a novel framework called SFMap, which estimates names of HTTPS servers by analyzing precedent DNS queries/responses in a statistical way. The SFMap framework introduces domain name graph, which can characterize highly dynamic and diverse nature of DNS mechanisms. Such complexity arises from the recent deployment and implementation of DNS ecosystems; i.e., canonical name tricks used by CDNs, the dynamic and diverse nature of DNS TTL settings, and incomplete and unpredictable measurements due to the existence of various DNS caching instances. First, we demonstrate that SFMap establishes good estimation accuracies and outperforms a state-of-the-art approach. We also aim to identify the optimized setting of the SFMap framework. Next, based on the preliminary analysis, we introduce techniques to make the SFMap framework scalable to large-scale traffic data. We validate the effectiveness of the approach using large-scale Internet traffic. (C) 2016 Elsevier B.V. All rights reserved.

Cyber attackers abuse the domain name system (DNS) to mystify their attack ecosystems, they systematically generate a huge volume of distinct domain names to make it infeasible for blacklisting approaches to keep up with newly generated malicious domain names. As a solution to this problem, we propose a system for discovering malicious domain names that will likely be abused in future. The key idea with our system is to exploit temporal variation patterns (TVPs) of domain names. The TVPs of domain names include information about how and when a domain name has been listed in legitimate/popular and/or malicious domain name lists. On the basis of this idea, our system actively collects DNS logs, analyzes their TVPs, and predicts whether a given domain name will be used for malicious purposes. Our evaluation revealed that our system can predict malicious domain names 220 days beforehand with a true positive rate of 0.985.

Modern web users may encounter a browser security threat called drive-by-download attacks when surfing on the Internet. Drive-by-download attacks make use of exploit codes to take control of user's web browser. Many web users do not take such underlying threats into account while clicking URLs. URL Blacklist is one of the practical approaches to thwarting browser-targeted attacks. However, URL Blacklist cannot cope with previously unseen malicious URLs. Therefore, to make a URL blacklist effective, it is crucial to keep the URLs updated. Given these observations, we propose a framework called automatic blacklist generator (AutoBLG) that automates the collection of new malicious URLs by starting from a given existing URL blacklist. The primary mechanism of AutoBLG is expanding the search space of web pages while reducing the amount of URLs to be analyzed by applying several pre-filters such as similarity search to accelerate the process of generating blacklists. AutoBLG consists of three primary components: URL expansion, URL filtration, and URL verification. Through extensive analysis using a high-performance web client honeypot, we demonstrate that AutoBLG can successfully discover new and previously unknown drive-by-download URLs from the vast web space.

Modern web users may encounter a browser security threat called drive-by-download attacks when surfing on the Internet. Drive-by-download attacks make use of exploit codes to take control of user's web browser. Many web users do not take such underlying threats into account while clicking URLs. URL Blacklist is one of the practical approaches to thwarting browser-targeted attacks. However, URL Blacklist cannot cope with previously unseen malicious URLs. Therefore, to make a URL blacklist effective, it is crucial to keep the URLs updated. Given these observations, we propose a framework called automatic blacklist generator (AutoBLG) that automates the collection of new malicious URLs by starting from a given existing URL blacklist. The primary mechanism of AutoBLG is expanding the search space of web pages while reducing the amount of URLs to be analyzed by applying several pre-filters such as similarity search to accelerate the process of generating blacklists. AutoBLG consists of three primary components: URL expansion, URL filtration, and URL verification. Through extensive analysis using a high-performance web client honeypot, we demonstrate that AutoBLG can successfully discover new and previously unknown drive-by-download URLs from the vast web space.

Since it is not hard to repackage an Android app, there are many cloned apps, which we call "clones" in this work. As previous studies have reported, clones are generated for bad purposes by malicious parties, e.g., adding malicious functions, injecting/replacing advertising modules, and piracy. Besides such clones, there are legitimate, similar apps, which we call "relatives" in this work. These relatives are not clones but are similar in nature; i.e., they are generated by the same app-building service or by the same developer using a same template. Given these observations, this paper aims to answer the following two research questions: (RQ1) How can we distinguish between clones and relatives? (RQ2) What is the breakdown of clones and relatives in the official and third-party marketplaces? To answer the first research question, we developed a scalable framework called APPraiser that systematically extracts similar apps and classifies them into clones and relatives. We note that our key algorithms, which leverage sparseness of the data, have the time complexity of O(n) in practice. To answer the second research question, we applied the APPraiser framework to the over 1 3 millions of apps collected from official and third-party marketplaces. Our analysis revealed the following findings: In the official marketplace, 79% of similar apps were attributed to relatives while, in the third-party marketplace, 50% of similar apps were attributed to clones. The majority of relatives are apps developed by prolific developers in both marketplaces. We also found that in the third-party market, of the clones that were originally published in the official market, 76% of them are malware. To the best of our knowledge, this is the first work that clarified the breakdown of "similar" Android apps, and quantified their origins using a huge dataset equivalent to the size of official market.

Since it is not hard to repackage an Android app, there are many cloned apps, which we call "clones" in this work. As previous studies have reported, clones are generated for bad purposes by malicious parties, e.g., adding malicious functions, injecting/replacing advertising modules, and piracy. Besides such clones, there are legitimate, similar apps, which we call "relatives" in this work. These relatives are not clones but are similar in nature; i.e., they are generated by the same app-building service or by the same developer using a same template. Given these observations, this paper aims to answer the following two research questions: (RQ1) How can we distinguish between clones and relatives? (RQ2) What is the breakdown of clones and relatives in the official and third-party marketplaces? To answer the first research question, we developed a scalable framework called APPraiser that systematically extracts similar apps and classifies them into clones and relatives. We note that our key algorithms, which leverage sparseness of the data, have the time complexity of O(n) in practice. To answer the second research question, we applied the APPraiser framework to the over 1 3 millions of apps collected from official and third-party marketplaces. Our analysis revealed the following findings: In the official marketplace, 79% of similar apps were attributed to relatives while, in the third-party marketplace, 50% of similar apps were attributed to clones. The majority of relatives are apps developed by prolific developers in both marketplaces. We also found that in the third-party market, of the clones that were originally published in the official market, 76% of them are malware. To the best of our knowledge, this is the first work that clarified the breakdown of "similar" Android apps, and quantified their origins using a huge dataset equivalent to the size of official market.

Most modern Internet services are carried over the web. A significant amount of web transactions is now encrypted and the transition to encryption has made it difficult for network operators to understand traffic mix. Thegoal of this study is to enable network operators to inferhostnames within HTTPS traffic because hostname information is useful to understand the breakdown of encrypted web traffic. The proposed approach correlates HTTPS flows and DNS queries/responses. Although this approach may appear trivial, recent deployment and implementation ofDNS ecosystems have made it a challenging research problem; i. e., canonical name tricks used by CDNs, the dynamic and diverse nature of DNS TTL settings, and incompletemeasurements due to the existence of various caching mechanisms. To tackle these challenges, we introduce domain name graph (DNG), which is a formal expression that characterizes the highly dynamic and diverse nature of DNS mechanisms. Furthermore, we have developed a framework called ServiceFlow map (SFMap) that works on top of the DNG. SFMap statistically estimates the hostname of an HTTPS server, given a pair of client and server IP addresses. We evaluate the performance ofSFMapthrough extensive analysis using real packet traces collected from two locations with different scales. Wedemonstrate thatSFMapestablishes good estimation accuracies and outperforms a stateoftheart approach.

A Darknet is a passive sensor system that monitors traffic routed to unused IP address space. Darknets have been widely used as tools to detect malicious activities such as propagating worms, thanks to the useful feature that most packets observed by a darknet can be assumed to have originated from non-legitimate hosts. Recent commoditization of Internet-scale survey traffic originating from legitimate hosts could overwhelm the traffic that was originally supposed to be monitored with a darknet. Based on this observation, we posed the following research question: "Can the Internet-scale survey traffic become noise when we analyze darknet traffic?" To answer this question, we propose a novel framework, ID2, to increase the darkness of darknet traffic, i.e., ID2 discriminates between Internet-scale survey traffic originating from legitimate hosts and other traffic potentially associated with malicious activities. It leverages two intrinsic characteristics of Internet-scale survey traffic: a network-level property and some form of footprint explicitly indicated by surveyors. When we analyzed darknet traffic using ID2, we saw that Internet-scale traffic can be noise. We also demonstrated that the discrimination of survey traffic exposes hidden traffic anomalies, which are invisible without using our technique.

To automate mal ware analysis, dynamic malware analysis systems have attracted increasing attention from both the industry and research communities. Of the various logs collected by such systems, the API call is a very promising source of information for characterizing mal ware behavior. This work aims to extract similar mal ware samples automatically using the concept of "API call topics;" which represents a set of API calls that are intrinsic to a specific group of malware samples. We first convert Win32 API calls into "API words." We then apply non-negative matrix factorization (NMF) clustering analysis to the corpus of the extracted API words. NMF automatically generates the API call topics from the API words. The contributions of this work can be summarized as follows. We present an unsupervised approach to extract API call topics from a large corpus of API calls. Through analysis of the API call logs collected from thousands of mal ware samples, we demonstrate that the extracted API call topics can detect similar malware samples. The proposed approach is expected to be useful for automating the process of analyzing a huge volume of logs collected from dynamic malware analysis systems.

Most modern Internet services are carried over the web. A significant amount of web transactions is now encrypted and the transition to encryption has made it difficult for network operators to understand traffic mix. Thegoal of this study is to enable network operators to inferhostnames within HTTPS traffic because hostname information is useful to understand the breakdown of encrypted web traffic. The proposed approach correlates HTTPS flows and DNS queries/responses. Although this approach may appear trivial, recent deployment and implementation ofDNS ecosystems have made it a challenging research problem; i. e., canonical name tricks used by CDNs, the dynamic and diverse nature of DNS TTL settings, and incompletemeasurements due to the existence of various caching mechanisms. To tackle these challenges, we introduce domain name graph (DNG), which is a formal expression that characterizes the highly dynamic and diverse nature of DNS mechanisms. Furthermore, we have developed a framework called ServiceFlow map (SFMap) that works on top of the DNG. SFMap statistically estimates the hostname of an HTTPS server, given a pair of client and server IP addresses. We evaluate the performance ofSFMapthrough extensive analysis using real packet traces collected from two locations with different scales. Wedemonstrate thatSFMapestablishes good estimation accuracies and outperforms a stateoftheart approach.

Modern web users are exposed to a browser security threat called drive-by-download attacks that occur by simply visiting a malicious Uniform Resource Locator (URL) that embeds code to exploit web browser vulnerabilities. Many web users tend to click such URLs without considering the underlying threats. URL blacklists are an effective countermeasure to such browser-targeted attacks. URLs are frequently updated; therefore, collecting fresh malicious URLs is essential to ensure the effectiveness of a URL blacklist. We propose a framework called automatic blacklist generator (AutoBLG) that automatically identifies new malicious URLs using a given existing URL blacklist. The key idea of AutoBLG is expanding the search space of web pages while reducing the amount of URLs to be analyzed by applying several pre-filters to accelerate the process of generating blacklists. AutoBLG comprises three primary primitives: URL expansion, URL filtration, and URL verification. Through extensive analysis using a high-performance web client honeypot, we demonstrate that AutoBLG can successfully extract new and previously unknown drive-by-download URLs.

A Darknet is a passive sensor system that monitors traffic routed to unused IP address space. Darknets have been widely used as tools to detect malicious activities such as propagating worms, thanks to the useful feature that most packets observed by a darknet can be assumed to have originated from non-legitimate hosts. Recent commoditization of Internet-scale survey traffic originating from legitimate hosts could overwhelm the traffic that was originally supposed to be monitored with a darknet. Based on this observation, we posed the following research question: "Can the Internet-scale survey traffic become noise when we analyze darknet traffic?" To answer this question, we propose a novel framework, ID2, to increase the darkness of darknet traffic, i.e., ID2 discriminates between Internet-scale survey traffic originating from legitimate hosts and other traffic potentially associated with malicious activities. It leverages two intrinsic characteristics of Internet-scale survey traffic: a network-level property and some form of footprint explicitly indicated by surveyors. When we analyzed darknet traffic using ID2, we saw that Internet-scale traffic can be noise. We also demonstrated that the discrimination of survey traffic exposes hidden traffic anomalies, which are invisible without using our technique.

Understanding the impacts and patterns of network events such as link flaps or hardware errors is crucial for diagnosing network anomalies. In large production networks, analyzing the log messages that record network events has become a challenging task due to the following two reasons. First, the log messages are composed of unstructured text messages generated by vendor-specific rules. Second, network equipment such as routers, switches, and RADIUS severs generate various log messages induced by network events that span across several geographical locations, network layers, protocols, and services. In this paper, we have tackled these obstacles by building two novel techniques: statistical template extraction (STE) and log tensor factorization (LTF). STE leverages a statistical clustering technique to automatically extract primary templates from unstructured log messages. LTF aims to build a statistical model that captures spatial-temporal patterns of log messages. Such spatial-temporal patterns provide useful insights into understanding the impacts and root cause of hidden network events. This paper first formulates our problem in a mathematical way. We then validate our techniques using massive amount of network log messages collected from a large operating network. We also demonstrate several case studies that validate the usefulness of our technique.

Understanding the impacts and patterns of network events such as link flaps or hardware errors is crucial for diagnosing network anomalies. In large production networks, analyzing the log messages that record network events has become a challenging task due to the following two reasons. First, the log messages are composed of unstructured text messages generated by vendor-specific rules. Second, network equipment such as routers, switches, and RADIUS severs generate various log messages induced by network events that span across several geographical locations, network layers, protocols, and services. In this paper, we have tackled these obstacles by building two novel techniques: statistical template extraction (STE) and log tensor factorization (LTF). STE leverages a statistical clustering technique to automatically extract primary templates from unstructured log messages. LTF aims to build a statistical model that captures spatial-temporal patterns of log messages. Such spatial-temporal patterns provide useful insights into understanding the impacts and root cause of hidden network events. This paper first formulates our problem in a mathematical way. We then validate our techniques using massive amount of network log messages collected from a large operating network. We also demonstrate several case studies that validate the usefulness of our technique. © 2014 IEEE.

We have proposed a method of identifying superspreaders by flow sampling and a method of filtering legitimate hosts from the identified superspreaders using a white list. However, the problem of how to optimally set parameters of phi, the measurement period length, m*, the identification threshold of the flow count m within phi, and H*, the identification probability for hosts with m = m*, remained unsolved. These three parameters seriously impact the ability to identify the spread of infection. Our contributions in this work are two-fold: (1) we propose a method of optimally designing these three parameters to satisfy the condition that the ratio of the number of active worm-infected hosts divided by the number of all vulnerable hosts is bound. by a given upper-limit during the time T required to develop a patch or an anti-worm vaccine, and (2) the proposed method can optimize the identification accuracy of worm-infected hosts by maximally using a limited amount of memory resource of monitors.

We have proposed a method of identifying superspreaders by flow sampling and a method of filtering legitimate hosts from the identified superspreaders using a white list. However, the problem of how to optimally set parameters of phi, the measurement period length, m*, the identification threshold of the flow count m within phi, and H*, the identification probability for hosts with m = m*, remained unsolved. These three parameters seriously impact the ability to identify the spread of infection. Our contributions in this work are two-fold: (1) we propose a method of optimally designing these three parameters to satisfy the condition that the ratio of the number of active worm-infected hosts divided by the number of all vulnerable hosts is bound. by a given upper-limit during the time T required to develop a patch or an anti-worm vaccine, and (2) the proposed method can optimize the identification accuracy of worm-infected hosts by maximally using a limited amount of memory resource of monitors.

The number of users of video on demand (VoD) services has increased dramatically. In VoD services, the demand for content items changes greatly hour to hour. Because service providers are required to maintain a stable service during peak hours, they need to design system resources based on the demand at peak time, so reducing the server load at this time is important. Although multicast delivery, in which multiple users requesting the same content item are supported by one delivery session, is effective for suppressing the server load during peak hours, user response times can increase greatly. A peer-to-peer-assisted delivery system, in which users download content items from other users watching the same content item, is also effective for reducing server load. However, system performance depends on selfish user behavior, and optimizing the usage of system resources is difficult. Moreover, complex operation, i.e., switching the delivery multicast tree or source peers, is necessary to support video cassette recorder (VCR) operation, e.g., fast forward, rewind, and pause. In this paper, we propose to reduce server load without increasing user response time by multicasting popular content items to all users independent of actual requests as well as providing on-demand unicast delivery. Through a numerical evaluation that uses actual VoD access log data, we clarify the effectiveness of the proposed method.

We consider the mean-variance relationship of the number of flows in traffic aggregation, where flows are divided into several groups randomly, based on a predefined flow aggregation index, such as source IP address. We first derive a quadratic relationship between the mean and the variance of the number of flows belonging to a randomly chosen traffic aggregation group. Note here that the result is applicable to sampled flows obtained through packet sampling. We then show that our analytically derived mean-variance relationship fits well those in actual packet trace data sets. Next, we present two applications of the mean-variance relationship to traffic management. One is an application to detecting network anomalies through monitoring a time series of traffic. Using the mean-variance relationship, we determine the traffic aggregation level in traffic monitoring so that it meets two predefined requirements on false positive and false negative ratios simultaneously. The other is an application to load balancing among network equipments that require per-flow management. We utilize the mean-variance relationship for estimating the processing capability required in each network equipment. (C) 2013 Elsevier B.V. All rights reserved.

The transmission bandwidth consumed by delivering rich content, such as movie files, is enormous, so it is urgent for ISPs to design an efficient delivery system minimizing the amount of network resources consumed. To serve users rich content economically and efficiently, an ISP itself should provide servers with huge storage capacities at a limited number of locations within its network. Therefore, we have investigated the content deployment method and the content delivery process that are desirable for this ISP-operated content delivery network (CDN). We have also proposed an optimum cache server allocation method for an ISP-operated CDN. In this paper, we investigate the properties of the topological locations of nodes at which cache placement is effective using 31 network topologies of actual ISPs. We also classify the 31 networks into two types and evaluate the optimum cache count in each network type.

In monitoring flows at routers for flow analysis or deep packet inspection, the monitor calculates hash values from the flow ID of each packet arriving at the input port of the router. Therefore, the monitors must update the flow table at the transmission line rate, so high-speed and high-cost memory, such as SRAM, is used for the flow table. This requires the monitors to limit the monitoring target to just some of the flows. However, if the monitors randomly select the monitoring targets, multiple routers on the route will sometimes monitor the same flow, or no monitors will monitor a flow. To maximize the number of monitored flows in the entire network, the monitors must select the monitoring targets while maintaining a balanced load among them. We propose an autonomous load-balancing method where monitors exchange information on monitor load only with adjacent monitors. Numerical evaluations using the actual traffic matrix of Internet2 show that the proposed method improves the total monitored flow count by about 50% compared with that of independent sampling. Moreover, we evaluate the load-balancing effect on 36 backbone networks of commercial ISPs. (c) 2012 Elsevier B.V. All rights reserved.

Web-based malware attacks have become one of the most serious threats that need to be addressed urgently. Several approaches that have attracted attention as promising ways of detecting such malware include employing one of several blacklists. However, these conventional approaches often fail to detect new attacks owing to the versatility of malicious websites. Thus, it is difficult to maintain up-to-date blacklists with information for new malicious websites. To tackle this problem, this paper proposes a new scheme for detecting malicious websites using the characteristics of IP addresses. Our approach leverages the empirical observation that IP addresses are more stable than other metrics such as URLs and DNS records. While the strings that form URLs or DNS records are highly variable, IP addresses are less variable, i.e., IPv4 address space is mapped onto 4-byte strings. In this paper, a lightweight and scalable detection scheme that is based on machine learning techniques is developed and evaluated. The aim of this study is not to provide a single solution that effectively detects web-based malware but to develop a technique that compensates the drawbacks of existing approaches. The effectiveness of our approach is validated by using real IP address data from existing blacklists and real traffic data on a campus network. The results demonstrate that our scheme can expand the coverage/accuracy of existing blacklists and also detect unknown malicious websites that are not covered by conventional approaches.

This paper introduces our recent results on mode-division multiplexing transmission with MIMO processing. We have been studying coherent optical MIMO transmission systems and developing few-mode fibers to reduce the complexity of MIMO processing, for example, by using multi-step index fibers to control the differential mode delay (DMD) of the fibers and to compensate for the total DMD. We also investigated a transmission system using reduced-complexity MIMO processing. Finally, we review our latest 2×2 WDM-MIMO transmission experiments with low MIMO processing complexity. © 2012 SPIE.

Content services that deliver large-volume content files have been growing rapidly. In these services, it is crucial for the service provider and the network operator to minimize traffic volume in order to lower the cost charged for bandwidth and the cost for network infrastructure, respectively. To reduce the traffic, traffic localization has been discussed; network traffic is localized when requested content files are served by an other nearby altruistic client instead of the source servers. With this mechanism, the concept of the peer-assisted content delivery network (CDN) can localize the overall traffic and enable service providers to minimize traffic without deploying or borrowing distributed storage. To localize traffic effectively, content files that are likely to be requested by many clients should be cached locally. We present a traffic engineering scheme for peer-assisted CDN models. Its key idea is to control the behavior of clients by using a content-oriented incentive mechanism. This approach optimizes traffic flows by letting altruistic clients download content files that are most likely to contribute to localizing network traffic. To let altruistic clients request the desired files, we combine content files while keeping the price equal to that for a single content. We discuss the performance of our proposed algorithm considering the cache replacement algorithms.

Web-based malware attacks have become one of the most serious threats that need to be addressed urgently. Several approaches that have attracted attention as promising ways of detecting such malware include employing one of several blacklists. However, these conventional approaches often fail to detect new attacks owing to the versatility of malicious websites. Thus, it is difficult to maintain up-to-date blacklists with information for new malicious websites. To tackle this problem, this paper proposes a new scheme for detecting malicious websites using the characteristics of IP addresses. Our approach leverages the empirical observation that IP addresses are more stable than other metrics such as URLs and DNS records. While the strings that form URLs or DNS records are highly variable, IP addresses are less variable, i.e., IPv4 address space is mapped onto 4-byte strings. In this paper, a lightweight and scalable detection scheme that is based on machine learning techniques is developed and evaluated. The aim of this study is not to provide a single solution that effectively detects web-based malware but to develop a technique that compensates the drawbacks of existing approaches. The effectiveness of our approach is validated by using real IP address data from existing blacklists and real traffic data on a campus network. The results demonstrate that our scheme can expand the coverage/accuracy of existing blacklists and also detect unknown malicious websites that are not covered by conventional approaches. © 2013 Information Processing Society of Japan.

Recently, the number of users downloading video content on the Internet has dramatically increased, and it is highly anticipated that downloading huge size, rich content such as movie files will become a popular use of the Internet in the near future. The transmission bandwidth consumed by delivering rich content is enormous, so it is urgent for ISPs to design an efficient delivery system that minimizes the amount of network resources consumed. To deliver web content efficiently, a content delivery network (CDN) is often used. CDN providers collocate a huge number of servers within multiple ISPs without being informed of detailed network information, i.e., network topologies, from ISPs. Minimizing the amount of network resources consumed is difficult because a CDN provider selects a server for each request based on only rough estimates of response time. Therefore, an ordinary CDN is not suited for delivering rich content. P2Pbased delivery systems are becoming popular as scalable delivery systems. However, by using a P2P-based system, we still cannot obtain the ideal delivery pattern that is optimal for ISPs because the server locations depend on users behaving selfishly. To provide rich content to users economically and efficiently, an ISP itself should optimally provide servers with huge storage capacities at a limited number of locations within its network. In this paper, we investigate the content deployment method, the content delivery process, and the server allocation method that are desirable for this ISP-operated CDN. Moreover, we evaluate the effectiveness of the ISPoperated CDN using the actual network topologies of commercial ISPs. Copyright © 2013 The Institute of Electronics, Information and Communication Engineers.

We analyze measured traffic data to investigate the characteristics of TCP quality metrics such as packet retransmission rate, roundtrip time (RTT), and throughput of connections classified by their type (client-server (C/S) or peer-to-peer (P2P)), or by the location of the connection host (domestic or overseas). Our findings are as follows. (i) The TCP quality metrics of the measured traffic data are not necessarily consistent with a theoretical formula proposed in a previous study. However, the average RTT and retransmission rate are negatively correlated with the throughput, which is similar to this formula. Furthermore, the maximum idle time, which is defined as the maximum length of the packet interarrival times, is negatively correlated with throughput. (ii) Each TCP quality metric of C/S connections is higher than that of P2P connections. Here "higher quality" means that either the throughput is higher, or the other TCP quality metrics lead to higher throughput
for example the average RTT is lower or the retransmission rate is lower. Specifically, the median throughput of C/S connections is 2.5 times higher than that of P2P connections in the incoming direction of domestic traffic. (iii) The characteristics of TCP quality metrics depend on the location of the host of the TCP connection. There are cases in which overseas servers might use a different TCP congestion control scheme. Even if we eliminate these servers, there is still a difference in the degree of impact the average RTT has on the throughput between domestic and overseas traffic. One reason for this is thought to be the difference in the maximum idle time, and another is the fact that congestion levels of these types of traffic differ, even if their average RTTs are the same. Copyright © 2013 The Institute of Electronics, Information and Communication Engineers.

In Video on Demand (VoD) services, the demand for content items greatly changes daily over the course of the day. Because service providers are required to maintain a stable service during peak hours, they need to design system resources on the basis of peak demand time, so reducing the server load at peak times is important. To reduce the peak load of a content server, we propose to multicast popular content items to all users independently of actual requests as well as providing on-demand unicast delivery. With this solution, however, the hit ratio of pre-distributed content items is small, and large-capacity Storage is required at each set-top box (STB). We can expect to cope with this problem by limiting the number of pre-distributed content items or clustering users based on their viewing histories. We evaluated the effect of these techniques by using actual VoD access log data. We also evaluated the total cost of the multicast pre-distribution VoD system with the proposed two techniques.

Web-based malware attacks have become one of the most serious threats that need to be addressed urgently. Several approaches that have attracted attention as promising ways of detecting such malware include employing one of several blacklists. However, these conventional approaches often fail to detect new attacks owing to the versatility of malicious websites. Thus, it is difficult to maintain up-to-date blacklists with information for new malicious websites. To tackle this problem, this paper proposes a new scheme for detecting malicious websites using the characteristics of IP addresses. Our approach leverages the empirical observation that IP addresses are more stable than other metrics such as URLs and DNS records. While the strings that form URLs or DNS records are highly variable, IP addresses are less variable, i.e., IPv4 address space is mapped onto 4-byte strings. In this paper, a lightweight and scalable detection scheme that is based on machine learning techniques is developed and evaluated. The aim of this study is not to provide a single solution that effectively detects web-based malware but to develop a technique that compensates the drawbacks of existing approaches. The effectiveness of our approach is validated by using real IP address data from existing blacklists and real traffic data on a campus network. The results demonstrate that our scheme can expand the coverage/accuracy of existing blacklists and also detect unknown malicious websites that are not covered by conventional approaches. © 2013 Information Processing Society of Japan.

In content services where people purchase and download large-volume contents, minimizing network traffic is crucial for the service provider and the network operator since they want to lower the cost charged for bandwidth and the cost for network infrastructure, respectively. Traffic localization is an effective way of reducing network traffic. Network traffic is localized when a client can obtain the requested content files from other a near-by altruistic client instead of the source servers. The concept of the peer-assisted content distribution network (CDN) can reduce the overall traffic with this mechanism and enable service providers to minimize traffic without deploying or borrowing distributed storage. To localize traffic effectively, content files that are likely to be requested by many clients should be cached locally. This paper presents a novel traffic engineering scheme for peer-assisted CDN models. Its key idea is to control the behavior of clients by using content-oriented incentive mechanism. This approach enables us to optimize traffic flows by letting altruistic clients download content files that are most likely contributed to localizing traffic among clients. In order to let altruistic clients request the desired files, we combine content files while keeping the price equal to the one for a single content. This paper presents a solution for optimizing the selection of content files to be combined so that cross traffic in a network is minimized. We also give a model for analyzing the upper-bound performance and the numerical results.

We investigate the impact of traffic on the performance of large-scale NAT (LSN). since it has been attracting attention as a means of better utilizing the limited number of global IPv4 addresses. We focus on the number of active flows because they drive up the LSN memory requirements in two ways; more flows must be held in LSN memory, and more global IPv4 addresses must be prepared. Through traffic measurement data analysis, we found that more than 1% of hosts generated more than 100 TCP flows or 486 UDP flows at the same time, and on average, there were 1.43-3.99 active TCP flows per host, when the inactive timer used to clear the flow state from a flow table was set to 15s. When the timer is changed from 15 s to 10 min, the number of active flows increases more than tenfold. We also investigate how to reduce the above impact on LSN in terms of saving memory space and accommodating more users for each global IPv4 address. We show that to save memory space, regulating network anomalies can reduce the number of active TCP flows on an LSN by a maximum of 48.3% and by 29.6% on average. We also discuss the applicability of a batch flow-arrival model for estimating the variation in the number of active flows, when taking into account that the variation is needed to prepare an appropriate memory space. One way to allow each global IPv4 address to accommodate more users is to better utilize destination IP address information when mapping a source IP address from a private address to a global IPv4 address. This can effectively reduce the required number of global IPv4 addresses by 85.9% for TCP traffic and 91.9% for UDP traffic on average.

Internet threats caused by botnets/worms are one of the most important security issues to be addressed. Darknet, also called a dark IP address space, is one of the best solutions for monitoring anomalous packets sent by malicious software. However, since darknet is deployed only on an inactive IP address space, it is an inefficient way for monitoring a Working network that has a considerable number of active IP addresses. The present paper addresses this problem. We propose a scalable, lightweight malicious packet monitoring system based on a multi-dimensional IP/port analysis. Our system significantly extends the monitoring scope of darknet. In order to extend the capacity of darknet, our approach leverages the active IP address space without affecting legitimate traffic. Multidimensional monitoring enables the monitoring of TCP ports with firewalls enabled on each of the IP addresses. We focus on delays of TCP syn/ack responses in the traffic. We locate syn/ack delayed packets and forward them to sensors or honeypots for further analysis. We also propose a policy-based flow classification and forwarding mechanism and develop a prototype of a monitoring system that implements our proposed architecture. We deploy our system on a campus network and perform several experiments for the evaluation of our system. We verify that our system can cover 89% of the IP addresses while darknet-based monitoring only covers 46%. On our campus network, our system monitors twice as many IP addresses as darknet.

We quantitatively evaluate how sampling and spatio/temporal granularity in traffic monitoring affect the detectability of anomalous traffic. Those parameters also affect the monitoring burden, so network operators face a trade-off between the monitoring burden and detectability and need to know which are the optimal paramter values. We derive equations to calculate the false positive ratio and false negative ratio for given values of the sampling rate, granularity, statistics of normal traffic, and volume of anomalies to be detected. Specifically, assuming that the normal traffic has a Gaussian distribution, which is parameterized by its mean and standard deviation, we analyze how sampling and monitoring granularity change these distribution parameters. This analysis is based on observation of the backbone traffic, which exhibits spatially uncorrelated and temporally long-range dependence. Then we derive the equations for delectability. With those equations, we can answer the practical questions that arise in actual network operations: what sampling rate to set to find the given volume of anomaly, or, if the sampling is too high for actual operation, what granularity is optimal to find the anomaly for a given lower limit of sampling rate.

Web-based malware attacks have become one of the most serious threats that need to be addressed urgently. Several approaches that have attracted attention as promising ways of detecting such malware include employing various blacklists. However, these conventional approaches often fail to detect new attacks owing to the versatility of malicious websites. Thus, it is difficult to maintain up-to-date blacklists with information regarding new malicious websites. To tackle this problem, we propose a new method for detecting malicious websites using the characteristics of IP addresses. Our approach leverages the empirical observation that IP addresses are more stable than other metrics such as URL and DNS. While the strings that form URLs or domain names are highly variable, IP addresses are less variable, i.e., IPv4 address space is mapped onto 4-bytes strings. We develop a lightweight and scalable detection scheme based on the machine learning technique. The aim of this study is not to provide a single solution that effectively detects web-based malware but to develop a technique that compensates the drawbacks of existing approaches. We validate the effectiveness of our approach by using real IP address data from existing blacklists and real traffic data on a campus network. The results demonstrate that our method can expand the coverage/accuracy of existing blacklists and also detect unknown malicious websites that are not covered by conventional approaches.

Monitoring flows at routers for flow analysis or deep packet inspection requires the monitors to update monitored flow information at the transmission line rate and needs to use highspeed memory such as SRAM. Therefore, it is difficult to measure all flows, and the monitors need to limit the monitoring target to a part of the flows. However, if monitoring targets are randomly selected, an identical flow will be monitored at multiple routers on its route, or a flow will not be monitored at any routers on its route. To maximize the number of flows monitored in the entire network, the monitors are required to select the monitoring targets while maintaining a balanced load among the monitors. In this paper, we propose an autonomous load balancing method where monitors exchange monitor load information with only adjacent monitors.

A sophisticated ad hoc cloud computing environment (SpACCE) providing calculation capacity of PCs is proposed to facilitate distributed collaboration. Distributed collaboration is now indispensable in daily work and mainly occurs ad hoc in offices and laboratories. However, computer resources in offices and laboratories are under-utilized, while conventional cloud computing environments composed of dedicated servers are not suited to flexibly deploying applications ad hoc. A SpACCE can be built according to the needs that occur at any given time on a set of personal, i.e., non-dedicated, PCs and dynamically migrate a server for application sharing to another PC. CollaboTray, an application-sharing system, indispensable to share any application without modification, is employed to realize the migration of a server. By migrating a server, the redundant calculation capacity of PCs used for individual work can be utilized to produce a sophisticated ad hoc cloud computing environment, where the response time of the application shared among the users is improved. The level of calculation capacity required to execute the migration of a server and the effectiveness of the migration were clarified by building a SpACCE in a university research room. © 2012 IEEE.

We investigated the detection accuracy of network anomalies when using flow statistics obtained through packet sampling. Through a case study based on measurement data, we showed that network anomalies generating a large number of small flows, such as network scans or SYN flooding, become difficult to detect during packet sampling. We then developed an analytical model that enables us to quantitatively evaluate the effect of packet sampling and traffic conditions, such as anomalous traffic volume, on detection accuracy. We also investigated how the detection accuracy worsens when the packet sampling rate decreases. In addition, we show that, even with a low sampling rate, spatially partitioning monitored traffic into groups makes it possible to increase detection accuracy. We also developed a method of determining an appropriate number of partitioned groups, and we show its effectiveness. Copyright (C) 2011 John Wiley & Sons, Ltd.

In the Internet, video streaming services, in which users can enjoy videos at home, are becoming popular. Video streaming with high definition TV (HDTV) or ultra high definition video (UHDV) quality will be also provided and widely demanded in the future. However, the transmission bit-rate of high-quality video streaming is quite large, so generated traffic flows will cause link congestion. In the Internet, routes that packets take are determined using static link weights, so the network productivity, i.e., the maximum achievable throughout by the network, is determined by the capacity of a bottleneck link with the maximum utilization, although utilizations of many links remain low level. Therefore, when providing streaming services of rich content, i.e., videos with HDTV or UHDV quality, it is important to flatten the link utilization, i.e., reduce the maximum link utilization. We propose that ISPs use multiple servers to deliver rich content to balance the link utilization and propose server allocation and server selection methods for parallel delivery. We evaluate the effect of parallel delivery using 23 actual commercial ISP networks. (C) 2010 Elsevier B.V. All rights reserved.

MapReduceを実行する大規模分散システムの性能評価モデルの構築に向け,MapReduceによる大規模データ処理を実行した際にシステム全体に生じる負荷をネットワークの観点から分析したケーススタディを紹介する.

Traffic caused by P2P services dominates a large part of traffic on the Internet and imposes significant loads on the Internet, so reducing P2P traffic within networks is an important issue for ISPs. In particular, a huge amount of traffic is transferred within backbone networks; therefore reducing P2P traffic is important for transit ISPs to improve the efficiency of network resource usage and reduce network capital cost. To reduce P2P traffic, it is effective for ISPs to implement cache devices at some router ports and reduce the hop length of P2P flows by delivering the required content from caches. However, the design problem of cache locations and capacities has not been well investigated, although the effect of caches strongly depends on the cache locations and capacities. We propose an optimum design method of cache capacity and location for minimizing the total amount of P2P traffic based on dynamic programming, assuming that transit ISPs provide caches at transit links to access ISP networks. We apply the proposed design method to 31 actual ISP backbone networks and investigate the main factors determining cache efficiency. We also analyze the property of network structure in which deploying caches are effective in reducing P2P traffic for transit ISPs. We show that transit ISPs can reduce the P2P traffic within their networks by about 50-85% by optimally designing caches at the transit links to the lower networks. (C) 2010 Elsevier B.V. All rights reserved.

Due to integrated high-speed networks accommodating various types of services and applications, the quality of service (QoS) requirements for those networks have also become diverse. The network resources are shared by the individual service traffic in the integrated network. Thus, the QoS of all the services may be degraded indiscriminately when the network becomes congested due to a sudden increase in traffic for a particular service if there is no traffic engineering taking into account each service's QoS requirement. To resolve this problem, we present a method of controlling individual service traffic by using an overlay network, which makes it possible to flexibly add various functionalities. The overlay network provides functionalities to control individual service traffic, such as constructing an overlay network topology for each service, calculating the optimal route for the service's QoS, and caching the content to reduce traffic. Specifically, we present a method of overlay routing that is based on the Hedge algorithm, an online learning algorithm to guarantee an upper bound in the difference from the optimal performance. We show the effectiveness of our overlay routing through simulation analysis for various network topologies. © 2011 IEEE.

The number of users of VoD services in which users can request content delivery on demand has increased dramatically. In VoD services, the demand for content changes greatly daily. Because service providers are required to maintain a stable service during peak hours, they need to design the system resources based on the demand at the peak time, so reducing the server load at the peak time is an important issue. Although multicast delivery in which multiple users requesting the same content are supported by one delivery session is effective for suppressing the server load during peak hours, the response time of users seriously increases. A P2P-assisted delivery system in which users download content from other users watching the same content is also effective for reducing the server load. However, the system performance depends on selfish user behavior, and optimizing the usage of system resources is difficult. Moreover, complex operation, i.e., switching the delivery multicast tree or source peers, is necessary to support VCR operation. In this paper, we propose to reduce the server load without increasing user response time by multicasting popular content to all users independently of actual requests as well as providing on-demand unicast delivery. Through numerical evaluation using actual VoD access log data, we clarify the effectiveness of the proposed method. © 2011 IEEE.

Peer-assisted content distribution technologies have been attracting attention. By using not only server resources but also the resources of end hosts (i.e., peers), we can reduce the offered load on servers as well as utilization of the access bandwidth of the servers. However, offered traffi to the network may increase because the traffi exchanged between peers passes across the network. Specificall, if individual peers send traffi disregarding underlay network topology and traffi conditions, the peer-assisted content distribution method may cause excessive traffi offered to the network and poor application performance. We thus investigated the impact of traffi caused by peer-assisted content distribution on the underlay network. We found that although peer-assisted content distribution disregarding underlay network topology causes 80120% additional traffi compared with the optimal case, i.e., content distribution using cache servers allocated optimally in the network, using underlay network topology enables us to achieve almost the same efficien network resource utilization as the optimal case. We also found that the peer-assisted approach can adaptively cope with change in the traffi demand matrix because uploaders in the network are generated according to the demand matrix in a self-organizing manner. This is because peers that have downloaded the content become uploaders so many uploaders are generated in the area where a large number of content requests exist according to the traffi condition
therefore, the content delivery traffi can be localized. © 2011 IEEE.

In Video on Demand (VoD) services, the demand for content items greatly changes daily, so reducing the server load at the peak time is an important issue for ISPs to reduce the server cost. To achieve this goal, we proposed to reduce the server load by multicasting popular content items to all users independently of actual requests as well as providing on-demand unicast delivery. In this solution, however, the hit ratio of pre-distributed content items is small, and a large-capacity storage is required at set-top box (STB). We might be able to cope with this problem by limiting the number of pre-distributed content items or clustering users based on the history of viewing. We evaluate the effect of these techniques using actual VoD access log data. We clarify that the required storage capacity at STB can be halved while keeping the effect of server load reduction to about 80% by limiting pre-distributed content items, and user clustering is effective only when the cluster count is about two. © 2011 IEEE.

Due to integrated high-speed networks accommodating various types of services and applications, the quality of service (QoS) requirements for those networks have also become diverse. The network resources are shared by the individual service traffic in the integrated network. Thus, the QoS of all the services may be degraded indiscriminately when the network becomes congested due to a sudden increase in traffic for a particular service if there is no traffic engineering taking into account each service's QoS requirement. To resolve this problem, we present a method of controlling individual service traffic by using an overlay network, which makes it possible to flexibly add various functionalities. The overlay network provides functionalities to control individual service traffic, such as calculating the optimal route for each service's QoS. Specifically, we present a method of overlay routing that is based on the Hedge algorithm, an online learning algorithm to guarantee an upper bound in the difference from the optimal performance. We show the effectiveness of our overlay routing through simulation analysis for various network topologies.

E-mail sender authentication is a promising way of verifying the sources of e-mail messages. Since today's primary e-mail sender authentication mechanisms are designed as fully decentralized architecture, it is crucial for e-mail operators to know how other organizations are using and misusing them. This paper addresses the question "How is the DNS Sender Policy Framework (SPF), which is the most popular e-mail sender authentication mechanism, used and misused in the wild?" To the best of our knowledge, this is the first extensive study addressing the fundamental question. This work targets both legitimate and spamming domain names and correlates them with multiple data sets, including the e-mail delivery logs collected from medium-scale enterprise networks and various IP reputation lists. We first present the adoption and usage of DNS SPF from both global and local viewpoints. Next, we present empirically why and how spammers leverage the SPF mechanism in an attempt to pass a simple SPF authentication test. We also present that non-negligible volume of legitimate messages originating from legitimate senders will be rejected or marked as potential spam with the SPF policy set by owners of legitimate domains. Our findings will help provide (1) e-mail operators with useful insights for setting adequate sender or receiver policies and (2) researchers with the detailed measurement data for understanding the feasibility, fundamental limitations, and potential extensions to e-mail sender authentication mechanisms. Copyright © 2011 ACM.

近年のインターネットでは,DDoSなどの異常トラヒックが多発しており,ユーザへの影響を最小限にとどめるには,それら異常トラヒックの迅速かつ正確な検出・特定・対処が求められる.一般的な異常トラヒック検出法として,正常トラヒック量の統計値に基いて予め定められた閾値を監視トラヒック量が超えた事象を異常として検出とする手法がある.これらの手法において、トラヒック監視の手法選択やパラメータ設定が異常トラヒック検出精度に影響をもたらすため、適切な選択、設定を行う必要がある。筆者らは文献で、ルータやスイッチ等からのフロー情報によるトラヒック監視手法における、サンプリングレート設定および空間的監視粒度設定の検出精度への影響を評価した.本稿では,同手法における時間的監視粒度設定の異常検出精度への影響について理論的な評価を行う.時間的粒度の評価にあたり、正常トラヒックを周辺分布が正規分布であり,長時間相関をもつものとしてモデル化した上で,多次元正規分布によって,False Negative Ratio(FNR)の計算式を与えた.導出式の精度について,実トラヒックデータを用いて評価を行った.

The transmission bandwidth consumed by delivering rich content, such as movie files, is enormous, so it is urgent for ISPs to design an efficient delivery system minimizing the amount of network resources consumed. To serve users rich content economically and efficiently, an ISP itself should provide servers with huge storage capacities at a limited number of locations within its network. Therefore, we have investigated the content deployment method and the content delivery process that are desirable for this ISP-operated content delivery network (CDN). We have also proposed an optimum cache server allocation method for an ISP-operated CDN. In this paper, we investigate the properties of the topological locations of nodes at which cache placement is effective using 31 network topologies of actual ISPs. We also classify the 31 networks into two types and evaluate the optimum cache count in each network type. ©2010 IEEE.

Traffic caused by P2P services dominates a large part of traffic on the Internet and imposes significant loads on the Internet, so reducing P2P traffic within networks is an important issue for ISPs. In particular, a huge amount of traffic is transferred within backbone networks
therefore reducing P2P traffic is important for transit ISPs to improve the efficiency of network resource usage and reduce network capital cost. To reduce P2P traffic, it is effective for ISPs to implement cache devices at some router ports and reduce the hop length of P2P flows by delivering the required content from caches. However, the design problem of cache locations and capacities has not been well investigated, although the effect of caches strongly depends on the cache locations and capacities. We propose an optimum design method of cache capacity and location for minimizing the total amount of P2P traffic based on dynamic programming, assuming that transit ISPs provide caches at transit links to access ISP networks. We apply the proposed design method to 31 actual ISP backbone networks. ©2010 IEEE.

This work attempts to characterize network traffic flows originating from large-scale video sharing services such as YouTube. The key technical contributions of this paper are twofold. We first present a simple and effective methodology that identifies traffic flows originating from video hosting servers. The key idea behind our approach is to leverage the addressing/naming conventions used in large-scale server farms. Next, using the identified video flows, we investigate the characteristics of network traffic flows of video sharing services from a network service provider view. Our study reveals the intrinsic characteristics of the flow size distributions of video sharing services. The origin of the intrinsic characteristics is rooted on the differentiated service provided for free and premium membership of the video sharing services. We also investigate temporal characteristics of video traffic flows.

A Honeypot is a system that aims to detect and analyze malicious attacks attempted on a network in an interactive manner. Because the primary objective of a honeypot is to detect enemies without being known to them, it is important to hide its existence. However, as several studies have reported, exploiting the unique characteristics of hosts working on a consecutive IP addresses range easily reveals the existence of honeypots. In fact, there exist some anti-honeypot tools that intelligently probe IP address space to locate Internet security sensors including honeypots. In order to tackle this problem, we propose a system called DarkPots, that consists of a large number of virtualized honeypots using unused and nonconsecutive IP addresses in a production network. DarkPots enables us to deploy a large number of honeypots within an active IP space used for a production network
thus detection is difficult using existing probing techniques. In addition, by virtually classifying the unused IP addresses into several groups, DarkPots enables us to perform several monitoring schemes simultaneously. This function is meaningful because we can adopt more than one monitoring schemes and compare their results in an operating network. We design and implement a prototype of DarkPots and empirically evaluate its effectiveness and feasibility by concurrently performing three independent monitoring schemes in a high-speed campus network. The system successfully emulated 7,680 of virtualized honeypots on a backbone link that carries 500 Mbps - 1 Gbps of traffic without affecting legitimate traffic. Our key findings suggest: (1) active and interactive monitoring schemes provide more in-depth insights of malicious attacks, compared to passive monitoring approach in a quantitative way, and (2) randomly distributed allocation of IP addresses has an advantage over the concentrated allocation in that it can collect more information from malwares. These features are crucial in monitoring the security threats. © 2010 IEEE.

Video streaming with HDTV or UHDV quality will be provided and widely demanded in the future. However, the transmission bit-rate of high-quality video streaming is quite large, so generated traffic flows will cause link congestion. Therefore, when providing streaming services of rich content, it is important to flatten the link utilization, i.e., reduce the maximum link utilization. To achieve this goal, parallel video streaming in which ISPs use multiple servers to deliver rich content is effective. However, the effect of parallel video streaming depends on the network topology and link capacities. In this paper, we investigate the impact of network topologies on the effect of parallel video streaming using 23 actual commercial ISP networks, when optimally designing server locations and optimally selecting servers.

Traffic caused by P2P services dominates a large part of traffic on the Internet and imposes significant loads on the Internet, so reducing P2P traffic within networks is an important issue for ISPs. In particular, a huge amount of traffic is transferred within backbone networks; therefore reducing P2P traffic is important for transit ISPs to improve the efficiency of network resource usage and reduce network capital cost. To reduce P2P traffic, it is effective for ISPs to implement cache devices at some router ports and reduce the hop length of P2P flows by delivering the required content from caches. However, the design problem of cache locations and capacities has not been well investigated, although the effect of caches strongly depends on the cache locations and capacities. We propose an optimum design method of cache capacity and location for minimizing the total amount of P2P traffic based on dynamic programming, assuming that transit ISPs provide caches at transit links to access ISP networks. We apply the proposed design method to 31 actual ISP backbone networks.

We describe a method of adaptively controlling bandwidth allocation to flows for reducing the file transfer time of short flows without decreasing throughput of long-duration large flows. According to the rapid increase in Internet traffic volume, effective traffic engineering is increasingly required. Specifically, the traffic of long-duration large flows due to the use of peer-to-peer applications, for example, is a problem. Most conventional QoS controls allocate a fair-share bandwidth to each flow regardless of its duration. Thus, a long-duration large flow (such as a P2P flow) is allocated the same bandwidth as a short-duration flow (such as data from a Web page) in which the user is more sensitive to response time, i.e., file transfer time. As a result, long-duration large flows consume bandwidth over a long period and increase response times of short-duration flows, and conventional QoS methods do nothing to prevent this. In this paper, we therefore investigate a different approach, that is, a new form of bandwidth control that enables us to achieve better performance when handling short-duration flows while maintaining performance when handling long-duration flows. The basic idea is to tag packets of long-duration large flows according to traffic conditions and to give temporarily higher priority to nontagged packets during network congestion. We also show the effectiveness of our method through simulation. ©2009 IEEE.

The transmission bandwidth consumed by delivering rich content, such as movie files, is enormous, so it is urgent for ISPs to design an efficient delivery system minimizing the amount of network resources consumed. To efficiently deliver web content, a content delivery networks (CDNs) have been widely used. CDN providers collocate a huge number of servers within multiple ISPs without being informed the detailed network information, i.e., network topologies, from ISPs. Minimizing the amount of network resources consumed is difficult because a CDN provider selects a server for each request based on only rough estimates of response time. To serve users rich content economically and efficiently; an ISP itself should optimally provide servers with huge storage capacities at a limited number of locations within its network. In this paper, we investigate the content deployment method, the content delivery process, and the server allocation method that are desirable for this ISP-operated CDN. Moreover, we evaluate the effectiveness of the ISP-operated CDN using network topologies of actual ISPs.

We describe a method of adaptively controlling bandwidth allocation to flows for reducing the file transfer time of short flows without decreasing throughput of long-duration large flows. According to the rapid increase in Internet traffic volume, effective traffic engineering is increasingly required. Specifically, the traffic of long-duration large flows due to the use of peer-to-peer applications, for example, is a problem. Most conventional QoS controls allocate a fair-share bandwidth to each flow regardless of its duration. Thus, a long-duration large flow (such as a P2P flow) is allocated the same bandwidth as a short-duration flow (such as data from a Web page) in which the user is more sensitive to response time, i.e., file transfer time. As a result, long-duration large flows consume bandwidth over a long period and increase response times of short-duration flows, and conventional QoS methods do nothing to prevent this. In this paper, we therefore investigate a different approach, that is, a new form of bandwidth control that enables us to achieve better performance when handling short-duration flows while maintaining performance when handling long-duration flows. The basic idea is to tag packets of long-duration large flows according to traffic conditions and to give temporarily higher priority to nontagged packets during network congestion. We also show the effectiveness of our method through simulation.

As a promising solution to manage the huge workload of large-scale VoD services, managed peer-assisted CDN systems, such as P4P [25] has attracted attention. Although the approach works well in theory or in a controlled environment, to our best knowledge, there have been no general studies that address how actual peers can be incentivized in the wild Internet; thus, deployablity of the system with respect to incentives to users has been an open issue. With this background in mind, we propose a new business model that aims to make peer-assisted approaches more feasible. The key idea of the model is that users sell their idle resources back to ISPs. In other words, ISPs can leverage resources of cooperative users by giving them explicit incentives, e.g., virtual currency. We show the high-level framework of designing optimal incentive amount to users. We also analyze how incentives and other external factors affect the efficiency of the system through simulation. Finally, we discuss other fundamental factors that are essential for the deployability of managed peer-assisted model. We believe that the new business model and the insights obtained through this work are useful for assessing the practical design and deployment of managed peer-assisted CDNs.

インターネット上においてネットワークリソースの浪費や品質劣化を引き起こす異常トラヒックをトラヒック測定を通じて検知・制御する技術は,安心で快適な通信サービスを提供するために不可欠となっている.一方,ネットワークの大規模化・高速化に伴い,パケットサンプリングによる測定が注目されている.本稿では,サンプルパケット情報から異常トラヒックを検出するためのトラヒック測定分析手法について,関連研究動向の紹介を交えながら筆者らの研究内容について紹介する.また,各手法の実データ評価結果も示す.

We propose an algorithm for finding heavy hitters in terms of cardinality (the number of distinct items in a set) in massive traffic data using a small amount of memory. Examples of such cardinality heavy-hitters are hosts that send large numbers of flows, or hosts that communicate with large numbers of other hosts. Finding these hosts is crucial to the provision of good communication quality because they significantly affect the communications of other hosts via either malicious activities such as worm scans, spam distribution, or botnet control or normal activities such as being a member of a flash crowd or performing peer-to-peer (P2P) communication. To precisely determine the cardinality of a host we need tables of previously seen items for each host (e.g., flow tables for every host) and this may infeasible for a high-speed environment with a massive amount of traffic. In this paper, we use a cardinality estimation algorithm that does not require these tables but needs only a little information called the cardinality summary. This is made possible by relaxing the goal from exact counting to estimation of cardinality. In addition, we propose an algorithm that does not need to maintain the cardinality summary for each host, but only for partitioned addresses of a host. As a result, the required number of tables can be significantly decreased. We evaluated our algorithm using actual backbone traffic data to find the heavy-hitters in the number of flows and estimate the number of these flows. We found that while the accuracy degraded when estimating for hosts with few flows, the algorithm could accurately find the top-100 hosts in terms of the number of flows using a limited-sized memory. In addition, we found that the number of tables required to achieve a pre-defined accuracy increased logarithmically with respect to the total number of hosts, which indicates that our method is applicable for large traffic data for a very large number of hosts. We also introduce an application of our algorithm to anomaly detection. With actual traffic data, our method could successfully detect a sudden network scan.

Managing the performance at the flow level through traffic measurement is crucial for effective network management. With the rapid rise in link speeds, collecting all packets has become difficult, so packet sampling has been attracting attention as a scalable means of measuring flow statistics. In this paper, we firstly propose a method of estimating TCP flow rates of sampled flows through packet sampling, and then develop a method of detecting performance degradation at the TCP flow level from the estimated flow rates. In the method of estimating flow rates, we use sequence numbers of sampled packets, which make it possible to improve markedly the accuracy of estimating the flow rates of sampled flows. Using both an analytical model and measurement data, we show that this method gives accurate estimations. We also show that, by observing the estimated rates of sampled flows, we can detect TCP performance degradation. The method of detecting performance degradation is based on the following two findings: (i) sampled flows tend to have high flow-rates and (ii) when a link becomes congested, the performance of high-rate flows becomes degraded first. These characteristics indicate that sampled flows are sensitive to congestion, so we can detect performance degradation of flows that are sensitive to congestion by observing the rate of sampled flows. We also show the effectiveness of our method using measurement data.

Multicast is efficient transfer scheme for contents distribution to a large number of clients, but it is necessary to meet security issues. On the other hand, source authentication is one of the important techniques for protecting from malicious users that plot eavesdropping, masquerading and so on. Though many authentication schemes have been proposed, most of them are not suitable for practical multicast network. The design of scheme should meet robustness against unreliable network. In this paper, we expand the existent authentication scheme using erasure code, and propose the novel control mechanism that cooperates with data reconstruction process. In addition, we show the effectiveness of our proposal by computer simulation. ©2008 IEEE.

The authors have proposed a method of identifying superspreaders by flow sampling and a method of extracting worm-infected hosts from the identified superspreaders using a white list. However, the problem of how to optimally set parameters, phi, the measurement period length, m*, the identification threshold of the flow count m within phi, and H*, the identification probability for hosts with m = m*, remains unsolved. These three parameters seriously affect the worm-spreading property. In this paper, we propose a method of optimally designing these three parameters to satisfy the condition that the ratio of the number of active worm-infected hosts divided by the number of all the vulnerable hosts is bound by a given upper-limit during the time T required to develop a patch or an anti-worm vaccine.

We present a method of detecting network anomalies, such as DDoS (distributed denial of service) attacks and flash crowds, automatically in real time. We evaluated this method using measured traffic data and found that it successfully differentiated suspicious traffic. In this paper, we focus on cyclic traffic, which has a daily and/or weekly cycle, and show that the differentiation accuracy is improved by utilizing such a cyclic tendency in anomaly detection. Our method differentiates suspicious traffic that has different statistical characteristics from normal traffic. At the same time, it learns about cyclic large-volume traffic, such as traffic for network operations, and finally considers it to be legitimate.

With the rapid increase of link speed in recent years, packet sampling has become a very attractive and scalable means in collecting flow statistics; however, it also makes inferring original flow characteristics much more difficult. In this paper, we develop techniques and schemes to identify flows with a very large number of packets (also known as heavy-hitter flows) from sampled flow statistics. Our approach follows a two-stage strategy: We first parametrically estimate the original flow length distribution from sampled flows. We then identify heavy-hitter flows with Bayes' theorem, where the flow length distribution estimated at the first stage is used as an a priori distribution. Our approach is validated and evaluated with publicly available packet traces. We show that our approach provides a very flexible framework in striking an appropriate balance between false positives and false negatives when sampling frequency is given.

In this paper, we quantitatively evaluate how sampling decreases the detect-ability of anomalous traffic. We build equations to calculate the false positive ratio (FPR) and false negative ratio (FNR) for given values of the sampling rate, statistics of normal traffic, and volume of anomalies to be detected. We show that by changing the measurement granularity, we can detect anomalies even with a low sampling rate and give the equation to derive optimal granularity by using the relationship between the mean and variance of aggregated flows. With those equations, we can answer for the practical questions that arise in actual network operations; what sampling rate to set in order to find the given volume of anomaly, or, if the sampling is too high for actual operation, then what granularity is optimal to find the anomaly for a given lower limit of sampling rate.

Autonomous distributed systems comprising an overlay network on the Internet are proliferating as a new technology responsible for the next-generation Web. Due to the properties of such a large-scale system, it is difficult to measure the characteristics of the entire system without modifying the internal protocol of the applications. In this paper, by using a measurement method applicable to P2P applications and measured results obtained by applying this measurement method in part, a general-purpose method for estimating the size and behavior of the entire P2P network is proposed. Further, the present method is applied to a real P2P network and its effectiveness is presented by a specific example. (C) 2006 Wiley Periodicals, Inc.

We propose a tool for summarizing communication patterns between multiple hosts from traffic data with a small memory space, using a 2-D bitmap. Here, we focus on the communication pattern between pairs of source-destination hosts
these represents spatial communication patterns. By analyzing communication patterns using a bitmap, we can identify a super spreader, which is a host that sends packets to many destinations, or analyze the relationship between two source hosts. For the latter purpose, we present an application of the bitmap to calculate the similarity of hosts based on their peer-hosts patterns. © 2007 IEEE.

We investigate how to detect network anomalies using flow statistics obtained through packet sampling. First, we show that network anomalies generating a huge number of small flows, such as network scans or SYN flooding, become difficult to detect when we execute packet sampling. This is because such flows are more unlikely to be sampled than normal flows. As a solution to this problem, we then show that spatially partitioning the monitored traffic into groups and analyzing the traffic of individual groups can increase the detectability of such anomalies: We also show the effectiveness of the partitioning method using network measurement data. © 2007 IEEE.

Abusive traffic caused by worms is increasing severely in the Internet. In many cases, worm-infected hosts generate a huge number of flows of small size during a short time. To suppress the abusive traffic and prevent worms from spreading, identifying these "superspreaders" as soon as possible and coping with them, e.g., disconnecting them from the network, is important. This paper proposes a simple and adaptive method of identifying superspreaders by flow sampling. By satisfying the given memory size and the requirement for the processing time, the proposed method can adaptively optimize parameters according to changes in traffic patterns.

Traffic flow measurement is essential to implement QOS control in the Internet. Flow monitoring system collects and stores sampled How states in a flow table (FT) and the entries are renewed at every packet sampling. Entries in the FT are checked and removed when no packets are sampled within a predetermined timeout. We propose an efficient timeout checking mechanism based on checking a small number of entries selected randomly from the FT. Our proposed method aims to reduce the number of memory accesses dramatically and keep the memory size small. We evaluate our method and compare with the conventional method that checks all flow entries of the FT periodically. Our simulation and comparison results show that our method is able to reduce the number of memory access at a factor of 1000 with a small increase in memory size of approximately 10 percent.

We investigate the detection accuracy of network anomalies when we use flow statistics obtained through packet sampling. We have already shown, through a case study based on measurement data, that network anomalies generating a huge number of small flows, such as network scans or SYN flooding, become hard to detect when we perform packet sampling. In this paper, we first develop an analytical model that enables us to quantitatively evaluate the effect of packet sampling on the detection accuracy and then investigate why detection accuracy worsens when the packet sampling rate decreases. In addition, we show that, even with a low sampling rate, spatially partitioning the monitored traffic into groups makes it possible to increase the detection accuracy. We also develop a method of determining an appropriate number of partitioned groups and show its effectiveness.

A method of controlling the rate of long-duration large flows and its performance evaluation is described in this paper. Most conventional QoS controls allocate a fair-share bandwidth to each flow regardless of its duration. Thus, a long-duration large flow (such as a P2P flow) is allocated the same bandwidth as a short-duration flow (such as data from a Web page) in which the user is more sensitive to response time. As a result, long-duration flows will occupy the bandwidth over the long period and worsen response times of short-duration flows, and the conventional QoS methods do nothing to prevent this. We have, therefore, proposed a new form of QoS control that takes flow duration into account and assigns higher priority to the acceptance of shorter-duration flows. In this paper, we show through simulation that our method achieves high performance for short-duration flows without degrading the performance of long-duration flows. We also explain how to set parameters used in our method. Furthermore, we discuss the applicability of a packet-sampling technique to improve the method's scalability. © 2006 IEEE.

Unfairness among best-effort flows is a serious problem on the Internet. In particular, UDP flows or unresponsive flows that do not obey the TCP flow control mechanism can consume a large share of the available bandwidth. High-rate flows seriously affect other flows, so it is important to identify them and limit their throughput by selectively dropping their packets. As link transmission capacity increases and the number of active flows increases, however, capturing all packet information becomes more difficult. In this paper, we propose a novel method of identifying high-rate flows by using sampled packets. The proposed method simply identifies flows from which Y packets are sampled without timeout. The identification principle is very simple and the implementation is easy. We derive the identification probability for flows with arbitrary flow rates and obtain an identification curve that clearly demonstrates the accuracy of identification. The characteristics of this method are determined by three parameters: the identification threshold Y, the timeout coefficient K, and the sampling interval N. To match the experimental identification probability to the theoretical one and to simplify the identification mechanism, we should set K to the maximum allowable value. Although increasing Y improves the identification accuracy, both the required memory size and the processing power grow as Y increases. Numerical evaluation using an actual packet trace demonstrated that the proposed method achieves very high identification accuracy with a much simpler mechanism than that of previously proposed methods.

We propose a method to find N hosts that have the N highest cardinalities, where cardinality is the number of distinct items such as the number of flows, ports, or peer hosts. The method also estimates their cardinalities. While existing algorithms to find the top N frequent items can be directly applied to find N hosts that send the N largest numbers of packets through packet data stream, finding hosts that have the N highest cardinalities requires tables of previously seen items for each host to check whether an item of an arrival packet is new, which requires a lot of memory. Even if we use the existing cardinality estimation methods, we still need to have cardinality information about each host. In this paper, we use the property of cardinality estimation, in which the cardinality of intersections of multiple data sets can be estimated with cardinality information of each data set. Using the property, we propose an algorithm that does not need to maintain tables for each host, but only for partitioned addresses of a host and estimate the cardinality of a host as the intersection of cardinalities of partitioned addresses. We also propose a method to find top N hosts in cardinalities which is to be monitored to detect anomalous behavior in networks. We evaluate our algorithm through actual backbone traffic data. While the estimation accuracy of our scheme degrades for small cardinalities, as for the top 100 hosts, the accuracy of our algorithm with 4, 096 tables is almost the same as having tables of every hosts.

A method of controlling the rate of long-duration large flows and its performance evaluation is described in this paper. Most conventional QoS controls allocate a fair-share bandwidth to each flow regardless of its duration. Thus, a long-duration large flow (such as a P2P flow) is allocated the same bandwidth as a short-duration flow (such as data from a Web page) in which the user is more sensitive to response time. As a result, long-duration flows will occupy the bandwidth over the long period and worsen response times of short-duration flows, and the conventional QoS methods do nothing to prevent this. We have, therefore, proposed a new form of QoS control that takes flow duration into account and assigns higher priority to the acceptance of shorter-duration flows. In this paper, we show through simulation that our method achieves high performance for short-duration flows without degrading the performance of long-duration flows. We also explain how to set parameters used in our method. Furthermore, we discuss the applicability of a packet-sampling technique to improve the method&apos;s scalability.

A method of estimating TCP flow-rates of sampled flows through packet sampling is described in this paper. We use sequence numbers of sampled packets, which make it possible to improve markedly the accuracy of estimating the flow rates. Using an analytical model, we investigate how to set parameters such as packet sampling probability used in this method of estimation. As a remarkable result, we show that the estimation accuracy improves as the sampling probability decreases. Using measured data, we also show that this method gives accurate estimations. We also show that this estimation method enables us to detect performance degradation at the TCP flow level.

We propose a method of dimensioning and managing the bandwidth of a link on which flows with heterogeneous access-link bandwidths are aggregated. We use a processor-sharing queue model to develop a formula approximating the mean TCP file-transfer time of flows on an access link in such a situation. This only requires the bandwidth of the access link carrying the flows on which. we are focusing and the bandwidth and utilization of the aggregation link, each of which is easy to set or measure. We then extend the approximation to handle various factors affecting actual TCP behavior, such as the round-trip time and restrictions other than the access-link bandwidth and the congestion of the aggregation link. To do this, we define the virtual access-link bandwidth as the file-transfer speed of a flow when the utilization of the aggregation link is negligibly small. We apply the virtual access-link bandwidth in our approximation to estimate the TCP performance of a flow with increasing utilization of the aggregation link. This method of estimation is used as the basis for a method of dimensioning the bandwidth of a link such that the TCP performance is maintained, and for a method of managing the bandwidth by comparing the measured link utilization with an estimated threshold indicating degradation of the TCP performance. The accuracy of the estimates produced by our method is estimated through both computer simulation and actual measurement.

Peer-to-peer (P2P) applications have been expanding rapidly in recent years, and the contribution of P2P to present Internet traffic is close to that of the World Wide Web (WWW). In this study, the flow of WWW and P2P traffic is analyzed by network measurement. The characteristics of WWW and P2P are examined, especially in terms of the flow arrival interval, flow duration, flow size, and flow rate. Based on the results of the analysis, the effect of a P2P flow increase on the overall traffic characteristics is investigated. The results of this study will be utilized in network design, proposals for control procedures, and traffic modeling, considering the traffic characteristics of particular applications. © 2005 Wiley Periodicals, Inc.

Managing the performance at the flow level through traffic measurement is crucial for effective network management. On the other hand, with the rapid rise in link speeds, collecting all packets has become difficult, so packet sampling has been attracting attention as a scalable means of measuring flow statistics. We have therefore established a method of detecting performance degradation at the TCP flow level from sampled flow behaviors. The proposed method is based on the following two flow characteristics: (i) sampled flows tend to have high flowrates and (ii) when a link becomes congested, the performance of high-rate flows becomes degraded first. These characteristics indicate that sampled flows are sensitive to congestion, so we can detect performance degradation of flows that are sensitive to congestion by observing the rate of sampled flows. We also show the effectiveness of our method using measured data.

Analysing and modeling of traffic play a vital role in designing and controlling of networks effectively. To construct a practical traffic model that can be used for various networks, it is necessary to characterize aggregated traffic and user traffic. This paper investigates these characteristics and their relationship. Our analyses are based on a huge number of packet traces from five different networks on the Internet. We found that: (1) marginal distributions of aggregated traffic fluctuations follow positively skewed (non-Gaussian) distributions, which leads to the existence of "spikes", where spikes correspond to an extremely large value of momentary throughput, (2) the amount of user traffic in a unit of time has a wide range of variability, and (3) flows within spikes are more likely to be "elephant flows", where an elephant flow is an IP flow with a high volume of traffic. These findings are useful in constructing a practical and realistic Internet traffic model.

We propose a method of dimensioning and managing the bandwidth of a link on which flows arriving on access links that have heterogeneous bandwidths are aggregated. We start by developing a formula that approximates the mean TCP file-transfer time of a flow in such a situation. This only requires the bandwidth of the access link carrying the flow and the bandwidth and utilization of the aggregation link, each of which is easy to set or measure. We then extend the approximation to handle various factors that affect actual TCP behavior, such as round-trip time and restrictions other than the access-link bandwidth and congestion of the aggregation link in the end-to-end path of the flow. To do this, we define the virtual access-link bandwidth as the file-transfer speed of the flow when utilization of the aggregation link is negligibly small. We apply the virtual access-link bandwidth in the approximation to estimate the TCP performance of the flow with increasing utilization of the aggregation link. We use this method of estimation as the basis for a method of dimensioning the bandwidth of the link such that TCP performance is maintained and a method of managing bandwidth by comparing measured link utilization with the estimated threshold that indicates degradation of TCP performance. We also use simulation to analyze the accuracy of the estimates produced by our method.

Analysing and modeling of traffic play a vital role in designing and controlling of networks effectively. To construct a practical traffic model that can be used for various networks, it is necessary to characterize aggregated traffic and user traffic, This paper investigates these characteristics and their relationship. Our analyses are based on a huge number of packet traces from five different networks on the Internet. We found that: (1) marginal distributions of aggregated traffic fluctuations follow positively skewed (non-Gaussian) distributions, which leads to the existence of "spikes", where spikes correspond to an extremely large value of momentary throughput, (2) the amount of user traffic in a unit of time has a wide range of variability, and (3) flows within spikes are more likely to be "elephant flows", where an elephant flow is an IP flow with a high volume of traffic. These findings are useful in constructing a practical and realistic Internet traffic model.

オンラインでの商品の購入履歴情報は，ユーザの属性を示唆するプライバシ情報を含む．このため，多くのオークションサイトでは，ユーザの購入履歴情報の漏洩を防ぐために，落札者の仮名表示などのプライバシ保護メカニズムを備えた相互評価システムを運用している．しかし先行研究により，特定のオークションサイトにおいて標的ユーザの購入履歴を推測する攻撃が可能であることが示された．本稿では，より強いプライバシ保護メカニズムを備えるオークションサイトにおいても購入履歴推測攻撃が可能であるかを確認するため，標的ユーザの評価スコアと仮名の出現率を用いたより汎用性の高い推測手法を提案した．実サービスでの実験の結果，97.2%のユーザの購入履歴の推測が可能であった．このような脅威がある現状において，オークションユーザに対してプライバシに関する意識調査を実施した．この結果，自身の購入履歴が第三者に閲覧されると困ると答えた回答者が一定数存在し，その多くが，自身の購入履歴が漏洩しうる状態にあることを認識していないことが明らかになった．これらの結果から，オンラインオークションで発生しうる潜在的なプライバシ問題とユーザの認識に差異があることが判明した．最後に，これらの事実に基づき，オークションユーザとサービス提供者が実施可能な対策を検討した．

近年,スパムメールに添付されたマルウェア感染の被害が拡大している.マルウェア感染を目的としたメールは,一般的なスパムと比べて性質が異なると考えられる.本論文はマルウェア感染を目的としたスパムメールに着目し,その特徴を一般的なスパムメールとの比較を通して分析した.この分析の結果,マルウェア添付スパムメールは,一般的なスパムメールと異なるドメインやアドレス宛に送信される傾向があるなど,メールの送信活動に違いがあることがわかった.

IN2015-74

Androidアプリはリパッケージングによる改変が容易であるため,オリジナル版開発者に無許可で開発された類似アプリが広く流通している.それらには広告収入を目的とした海賊版やユーザを誘導する目的で作られたマルウェアが含まれる.本研究は公式およびサードパーティーマーケットで収集した大規模なAndroidアプリ群を解析し,正規のアプリに類似したアプリの実態を調査することを目的とする.またそのような調査を可能にするための解析手法を提案する.はじめにアプリのリソース情報を用いて正規アプリに外観が類似するアプリを抽出する.次に外観が類似したアプリのコードを解析し,オリジナルに対するコードの増分が持つ特徴によって類似アプリを分類する.このような解析により,効率的にリパッケージングアプリの抽出・分析が可能である.分析の結果,海賊版が無視できない量存在することやアプリに追加された悪質なモジュールの特徴などが明らかになった.

インターネット上で提供されるサービスの大多数はHTTPによるWeb通信である.そして多くのWeb通信がHTTPSによって暗号化されるようになった.例えばYouTubeのビデオも暗号化された通信路で配信されている.HTTPSの普及に伴いトラヒックの計測・分析が難しくなる問題が生じる.すなわち暗号化により通信の宛先サーバのホスト名が不明となるため,ネットワークサービス提供者は自網の回線がユーザにどのように利用されているかを把握することが困難となった.この問題を解決するためにDNSクエリ・応答情報を用いてHTTPS通信のホスト名を推定するフレームワークであるSFMap (Service-Flow map)を提案する.SFMapの主要なアイディアはCNAMEを経由したIPアドレスと複数ドメイン名の関係をグラフとして保持し,そのグラフを元に所与のサーバIPアドレス,クライアントIPアドレス情報から尤もらしいサーバのホスト名を推定することにある.2箇所の計測地点で収集した実トラヒックデータを用い,提案手法がこれまでの先行研究で提案された手法と比較してより高い精度を達成することを示す.

Webアプリケーションにおいてはドラィブバイダウンロードやフィッシング等,悪性サイトに誘導する媒介としてURLを利用する攻撃手法が存在する.WebユーザがURLの危険性を認識せずにそのような悪性サイトにアクセスすると様々な被害がもたらされる.URL Blacklistは上述の被害を食い止めるための有効な手段の一つである.しかしながらURL Blacklist構築における未解決の課題として,対象となるデータの超大規模化と,つねに変化し続けるURLをタイムリーな発見が挙げられる.本研究では,既存のURL Blacklistを拡張し,未知の悪性URLを効率的に抽出するシステムを提案する.提案システムの概要およびシステムの有効性を検証した結果を報告する.

In 2008, the anti-Malware engineering WorkShop (MWS) was organized in Japan. The main objective of MWS is to accelerate and expand the activities of anti-malware research. To this end, MWS aims to attract new researchers and stimulate new research by lowering the technical obstacles associated with collecting the datasets that are crucial for addressing recent cyber threats. Moreover, MWS hosts intimate research workshops where researchers can freely discuss their results obtained using MWS and other datasets. This paper presents a quantitative accounting of the effectiveness of the MWS community by tracking the number of papers and new researchers that have arisen from the use of our datasets. In addition, we share the lessons learned from our experiences over the past seven years of sharing datasets with the community. Copyright is held by the owner/author(s).

オーナーの関知しないところでカメラを秘密裏に濫用し,盗撮・盗聴や情報漏洩を試みる可能性があるAndroidアプリを自動的に検出する方法を提案する.主要なアイディアはアプリケーションパッケージファイルを逆アセンブルしたコードの解析とアプリの詳細を自然言語で記述したdescriptionのテキスト解析を組み合わせることである.サードパーティマーケットで収集した10,885のアプリケーションを対象に提案手法を適用したところ,カメラを秘密裏に濫用する可能性が高い43個の検体を自動的に検出した.手動による動的解析の結果, 43検体中少なくとも28検体はユーザに開示された正当な方法でカメラを利用していること,および2検体は内容と動作が不自然であり,かつユーザがカメラを利用する画面が認められないことからコードの詳細な静的解析が必要な検体であることがわかった.また43検体中18検体がマルウェアと判定されており,提案手法で抽出した記述と動作に齟齬があるアプリケーションは高い確率でマルウェアであることが示された.

オーナーの関知しないところでカメラを秘密裏に濫用し，盗撮・盗聴や情報漏洩を試みる可能性がある Android アプリを自動的に検出する方法を提案する．主要なアイディアはアプリケーションパッケージファイルを逆アセンブルしたコードの解析とアプリの詳細を自然言語で記述した description のテキスト解析を組み合わせることである．サードパーテイマーケットで収集した 10,885 のアプリケーションを対象に提案手法を適用したところ，カメラを秘密裏に濫用する可能性が高い 43 個の検体を自動的に検出した．手動による動的解析の結果，43 検体中少なくとも 28 検体はユーザに開示された正当な方法でカメラを利用していること，および 2 検体は内容と動作が不自然であり，かつユーザがカメラを利用する画面が認められないことからコードの詳細な静的解析が必要な検体であることがわかった．また 43 検体中 18 検体がマルウェアと判定されており，提案手法で抽出した記述と動作に齪酷があるアプリケーションは高い確率でマルウェアであることが示された。We propose a method for detecting Android apps that may secretly abuse the camera to leak private or important information of the user. Our key idea is to combine two approaches: (1) analysis of disassembled code of application package files and (2) text analysis of natural language descriptions that are used to explain the details of apps. In our experiment using 10,855 Android apps collected from third-party markets, our method successfully extracted 43 samples that likely abuse camera secretly. We applied dynamic analysis to the 43 samples manually and revealed that at least 28 samples did have proper reasons to use camera thus were negative and two samples exhibited unnatural content and behaviour; thus we need detailed static code analysis for further investigation. We also found that of the 43 samples, 18 samples were detected as malware; hence, a large fraction of detected samples with our framework, which aims to extract apps with inconsistency between description and code, were actually malware.

Web-based malware attacks have become one of the most serious threats that need to be addressed urgently. Several approaches that have attracted attention as promising ways of detecting such malware include employing one of several blacklists. However, these conventional approaches often fail to detect new attacks owing to the versatility of malicious websites. Thus, it is difficult to maintain up-to-date blacklists with information for new malicious websites. To tackle this problem, this paper proposes a new scheme for detecting malicious websites using the characteristics of IP addresses. Our approach leverages the empirical observation that IP addresses are more stable than other metrics such as URLs and DNS records. While the strings that form URLs or DNS records are highly variable, IP addresses are less variable, i.e., IPv4 address space is mapped onto 4-byte strings. In this paper, a lightweight and scalable detection scheme that is based on machine learning techniques is developed and evaluated. The aim of this study is not to provide a single solution that effectively detects web-based malware but to develop a technique that compensates the drawbacks of existing approaches. The effectiveness of our approach is validated by using real IP address data from existing blacklists and real traffic data on a campus network. The results demonstrate that our scheme can expand the coverage/accuracy of existing blacklists and also detect unknown malicious websites that are not covered by conventional approaches.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.21(2013) No.3 (online)DOI http://dx.doi.org/10.2197/ipsjjip.21.539------------------------------Web-based malware attacks have become one of the most serious threats that need to be addressed urgently. Several approaches that have attracted attention as promising ways of detecting such malware include employing one of several blacklists. However, these conventional approaches often fail to detect new attacks owing to the versatility of malicious websites. Thus, it is difficult to maintain up-to-date blacklists with information for new malicious websites. To tackle this problem, this paper proposes a new scheme for detecting malicious websites using the characteristics of IP addresses. Our approach leverages the empirical observation that IP addresses are more stable than other metrics such as URLs and DNS records. While the strings that form URLs or DNS records are highly variable, IP addresses are less variable, i.e., IPv4 address space is mapped onto 4-byte strings. In this paper, a lightweight and scalable detection scheme that is based on machine learning techniques is developed and evaluated. The aim of this study is not to provide a single solution that effectively detects web-based malware but to develop a technique that compensates the drawbacks of existing approaches. The effectiveness of our approach is validated by using real IP address data from existing blacklists and real traffic data on a campus network. The results demonstrate that our scheme can expand the coverage/accuracy of existing blacklists and also detect unknown malicious websites that are not covered by conventional approaches.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.21(2013) No.3 (online)DOI http://dx.doi.org/10.2197/ipsjjip.21.539------------------------------

ネットワーク機器が生成するsyslogなどのログは,故障原因分析や機器の詳細な状態把握に有用である.しかし,機器の増大やマルチペンダ化に伴うログの多様化,大規模化が進み,ログの分析そのものが課題となっている.本研究では,ログに関する事前知識を用いずに,効率的にネットワークオペレーションに有用な形で可視化する手法を提案する.提案手法は,ログのテンプレート化/グループ化といった情報圧縮による,大規模なログの俯瞰を実現する.また,ログの生起パターンに着目した異常性抽出により,異常ログの特定を容易にする.さらに,本研究ではある検証網で取得した実データを用いた提案手法の評価実験を行い,その有用性を示す.

コンテンツ配信サービスにおいて,コンテンツの大容量化に伴うNWトラピックの急激な増加が問題となっている,ピア補助型コンテンツ配信は,ソースサーバだけでなく利他的なユーザのキャッシュからもコンテンツを配信することで,トラピックの局所化を行う技術である.我々は,ピア補助型コンテンツ配信において,利他的ユーザがキャッシュをすることでトラピックの局所化が期待されるコンテンツを組み合わせ,1コンテンツと同一の価格で提供することで,利他的のダウンロードを誘導し,大幅なトラピック削減を行ってきた.しかしながら,組み合わせコンテンツを複数のユーザにキャッシュさせることで,更なるトラヒックの局所化が期待できる.本稿では,組み合わせコンテンツを提供する配信周期を決定するメカニズムを提案し,それに基づくセット配信による削減トラピック量を評価する.

データセンターにおける代表的な分散処理機構であるMapReduce処理では,シャッフルと呼ばれる大量のデータ転送が計算サーバ間で行われる.本稿では,計算サーバ間を相互接続するネットワークの構造がMapReduce処理におけるシャツフルの完了時間にどのような影響を与えるかについて考察する.特に本稿では,代表的なネットワーク構造として単純木構造とFat-Tree構造を取り上げ,理論的な検討並びにシミュレーション実験を併用して,相互接続網の構造がシャッフルの完了時間に与える影響について考察する.

多種多様なサービスが展開され大規模化した近年のIPネットワークでは,従来よりも高度かつ複雑なネットワーク監視が求められている.特にネットワーク機器が生成する種々のログ情報は障害分析や詳細な状態把握に有用である.しかし,機器の増大やマルチベンダ化に伴うログの多様化,大規模化が進み,ログの分析そのものが課題となっている.本研究では,過去に蓄積された膨大なログ情報を用いたログに関する事前知識を全く必要としないログのテンプレート抽出と,同時生起するテンプレートのグルーピングにより,ログ分析を行う上での意味のある情報を効率的に把握する手法を提案する.

近年,動画コンテンツを配信要求に応じてオンデマンドに配信するVoDサービスの普及が目覚ましい。VoDサービスにおいて,配信要求量は一日の周期で大きく変化するが,ピーク時の需要に対しても安定した品質でサービスを維持する必要があり,サービスプロバイダにとってピーク時のサーバ負荷低減が課題である.そこで筆者らは,全ユーザにコンテンツを配信要求とは無関係に事前にマルチキャスト配信することで,ユーザのレスポンス時間を増大させることなく,サーバの負荷低減を図ることを提案した。また実際のVbDシステムにおけるアクセスログデータを用いた評価を行い,提案方式の配信サーバ負荷低減効果を明らかにした.しかし,マルチキャスト事前配信法がネットワーク負荷に与える影響に関しては未検討である.そこで本稿では,様々なISPのネットワークトポロジを用いた評価を行い,ネットワーク負荷に与える影響を明らかにする.

大容量のコンテンツ配信サービスにおいては,帯域使用に課されるコストやインフラコストを削減するため,トラヒックの局所化が必要である.そこで,ソースサーバだけでなく利他的なユーザにコンテンツを転送させることで,局所化を実現するピア補助型コンテンツ配信が注目されている.しかし,トラヒックの局所化には,多くのユーザが要求すると期待されかつ近傍にキャッシュされていないコンテンツが利他的なユーザにキャッシュされている必要がある.そこで,トラヒックの局所化が期待できるコンテンツを利他的なユーザがダウンロードするよう,コンテンツを組み合わせて提供する方式を提案する.組み合わせられた複数のコンテンツの価格を1コンテンツと同一にすることで,ユーザに対しインセンティブを発生させる.本稿では,組み合わせを最適化する手法とその性能上界を示す.

カーネルマルウェアは独自のネットワークドライバを実装し，カーネルモードの通信を行うことで監視ツールからの隠匿を試みる．これらのネットワークドライバは独自の実装であるため，TCPヘッダのパラメータを分析することでカーネルマルウェア発のトラヒックフローを検出することができる．本研究ではこの性質に基づき，カーネルマルウェアの可能性があるTCPフィンガープリントを整理した．そのフィンガープリントを複数の実運用ネットワークに適用し，フルカーネルマルウェアに感染した可能性が高いホストおよびその通信の特徴を分析する．Modern kernel malwares compose of their own network drivers and use them directly from kernel-mode to conceal their activities from anti-malware tools. Since these network drivers have specific characteristics, we can detect traffic flows originating from those drivers by analyzing some parameters recorded in TCP headers. On the basis of the above characteristics, we apply a fingerprinting technique to collect IP addresses of the hosts that are likely infected with kernel malwares. Using the method, we also aim to understand the characteristics of the hosts infected with kernel malware and their communications using network measurement data collected in several production networks.

IPレピュテーションシステムは,過去の観測に基づいてIPアドレスの格付けを行い,外部からの問い合わせに応じて格付の結果を応答するサービスである.例えば電子メールの送信元に関する情報は,そのメッセージがスパムメールであるかを判別する上で非常に有効であることが知られており,スパム送信元のIPアドレスをまとめたデータベースであるDNSBL(Domain Name System Block List)がIPレピュテーションシステムとして広く運用されている.本研究は独立に管理される複数のIPレピュテーションシステムの応答結果を得たときに,それらの応答を統合することによって,より高精度な判別結果を得るための方式を提案する.電子メールの配送ログを用い,各方式の精度及び優劣の条件を評価した結果を報告する.

IPネットワーク上で様々なサービスが提供され,ネットワーク上における通信品質保証に対する要求が高まるにともない,トラヒック測定を通じて品質を把握することの重要性が増してきている.本稿では,実計測トラヒックデータを対象に,フロー種別毎にTCP品質特性を分析した結果,および,パケット再送率や平均RTT,スループットといったTCP品質尺度間における相関性を分析した結果を示す.第一に,クライアントサーバ通信とP2P通信ではクライアントサーバ通信の方が品質が良く,国内間の下り通信では2.5倍ほどスループットの中央値が高くなっていることを示す.次に,フローにおける再送率や平均RTTが低いほどスループットが高く,特に平均RTTの方がスループットに与える影響が強いこと,および同じ平均RTTでも,国内間での通信と国外との通信とでは輻輳の度合いが違うために,スループットへの影響にも差があることを定量的に示す.特に,国内間での通信の方が概ね国外との通信より品質が良いが,平均RTTが100msec付近では国外通信の方がスループットが高かったことを示す.

ピア補助型コンテンツ配信は,コンテンツを他のユーザのキャッシュから取得させることで,全コンテンツをソースサーバから取得させる場合に比べ,トラヒックを削減させる技術である.しかし,トラヒックを大きく削減するためには,多くのユーザによって要求されるようなコンテンツがキャッシュされている必要がある.また,コンテンツが配置される位置もトラヒックの削減量に大きな影響を与える.本稿では,コンテンツ価格を割引することで,ユーザがキャッシュされるべきコンテンツをダウンロードするよう誘導する方式を提案する.そして,シミュレーションにより提案方式による消費帯域の削減量を評価する.

フロー分析やDPI等のためにルータにおいてフロー測定を行う際に,ネットワーク全体の測定フロー数を最大化するには,測定負荷が可能な限り測定装置間で分散されるよう測定対象を選択する必要がある.そこで筆者らは以前,各測定装置が隣接測定装置とのみ情報を交換することで,自律的に測定装置間で測定負荷を均等化する負荷分散法を提案した.本稿では,公開されている36のISPのNWトポロジを用いた性能評価を行い,他の方式と比較した本方式の有効性を確認し,NWトポロジが本方式の効果に与える影響を明らかにする.

Web感染型マルウェアが猛威を振るっている.<br />マルウェアに感染すると個人情報が不正に盗み出されたり,Webサイトが改ざんされたりする.<br />マルウェアの中には,Webブラウザの脆弱性を突いて制御を奪い,Webサイトにアクセスした利用者を複数のWebサイトに誘導して,マルウェアをダウンロードさせたり,インストールさせるDrive-by-Download攻撃がある.<br />本論文は,パケットキャプチャデータに基づいてDrive-by-Download攻撃によるリダイレクトを解析する.<br />その結果を活用すると,マルウェアを配布するサイト,および踏台とされているサイト,入口となっているサイトを特定することができる.<br />このようなサイトの情報を集めてURLのブラックリストを構成すれば,表面上は正常なサイトにアクセスしているように見えるのに,実際には悪性サイトのURLに誘導されている場合でも検知することができる.

インターネットではマルウェア(悪意のあるソフトウェア)の活動による被害が<br />拡大している。今後、マルウェアがさらに複雑になり多様化すると予想される。<br />マルウェアによる被害を少くするためには、既知のマルウェアの情報に基づいて<br />防御するだけでなく、未知の攻撃に対する防衛手段を講じる必要がある。本論文<br />は教師あり機械学習法の一つであるSVM (Support Vector Machine) を用いて、<br />過去の悪意のある通信の特徴を事前に学習し、未来の通信の悪性を予測して判別<br />する手法を提案する。この方法は、既存のシグネチャベースのルールでは検知や<br />フィルタリングが難しい未知の攻撃通信の判別において優位性がある。

フロー分析やDPI等のためにルータにおいてフロー測定を行う際には,回線レートで取得フロー情報を更新する必要があり, SRAM等の高速なメモリを用いるため全フローを測定対象とすることは困難である.そのため各測定装置は測定対象を一部のフローに限定する必要があるが,各測定装置がランダムに測定対象を選択した場合,同一のフローが複数の経由測定装置で測定される可能性や,全く測定されない可能性がある.またネットワーク全体の測定フロー数を最大化するには,測定負荷が可能な限り測定装置間で分散されるよう測定対象を選択する必要がある.そこで本稿では,各測定装置が隣接測定装置とのみ情報を交換することで,自律的に測定装置間で測定負荷を均等化する負荷分散法を提案する.

エンドエンドでの遅延時間といった通信品質を把握する方法として,ある特定のエンドエンド間でアクティブ測定を行う代わりに,ネットワーク全体の通信品質やエリア間品質マトリクス把握を目的としたパッシブ測定による品質推定法を述べる.ここでは,エンドホスト間のround trip time(RTT)に注目する.スケール性を考慮して,ハッシュベースサンプリングやbloom filter(BF)を用い,全パケット測定せずにRTTを推定する.また,本手法を実測データに適用した結果も示す.

本研究は分散コンピューティングシステムにおいてMapReduceによる大規模データ処理を実行した際にシステム全体に生じるワークロードをネットワークの観点から分析した結果を報告する.12台の計算機で構成されるHadoopクラスタを利用し,Masterサーバおよび各々のSlaveサーバで取得したMapReduce Jobのログ,およびSlaveサーバ間の通信をキャプチャしたデータを収集した.はじめにMapReduceジョブを構成する各々のタスクとネットワークに生じ得る負荷の関係をケーススタディによって明らかにする.つぎに,MapReduceに与えるパラメタによって,ノード間のデータ転送に用いられるTCPフローのサイズ,持続時間,レートの分布が変わることを示す.最後にMapReduceジョブによるネットワーク負荷を計測する際に注意すべき点について論じる.

筆者らは以前,VoDシステムにおいてピーク時の配信サーバ負荷低減を目的に,ユニキャスト(UC)配信に加え配信要求とは無関係にコンテンツをマルチキャスト(MC)配信することを提案した.しかしパケット損が生じた場合の補償方式については未検討である.本稿では,ユーザが視聴するのはSTBにMC事前配信されたコンテンツのごく一部であることから,ユーザの視聴時に視聴コンテンツにおける欠損パケットのみをUCで再送することを提案する.実際のアクセスログデータを用いた数値評価により,従来方式と比較して,提案方式は損失補償のために生成されるトラヒック量を大幅に低減できることを示す.

IPv4アドレス枯渇問題への対処として注目されるlarge scale NAT (LSN)へのTCPトラヒックのインパクトを評価した.実トラヒック分析の結果,1%のホストが100フロー以上を同時に生成していた.また,測定ポイントに依存して同時フロー数は大きく異なり,測定点Aでは1ホスト当り平均で1.43-1.83フローなのに対し,測定点Bでは1ホスト当り3.10-3.98フローであった.また,フロークリアに用いるタイマーを15秒から10分に変更すると,フロー数は10倍以上に増加した.さらに,これらインパクトを軽減する方法についても評価した.まず,メモリ上で保持すべきフロー数の削減を目的として,ネットワークスキャンのような異常トラヒック規制の効果を評価した.その結果,最大で,48%同時フロー数を削減できた.また,LSNで必要なIPアドレス数削減を目的としてフローの宛先IPアドレス情報を利用して,LSNで割り当てるsrcIP:srcPort数を削減する方法について評価した.その結果,86%程度削減できることが確認できた.

電子メール送信者認証はメッセージ送信元に関する正当性の検証を実現する有望な技術である.今日の主要な電子メール送信者認証技術は完全に非集中・分散型であるため,同技術を利用する際には他の組織がどのように認証技術を使っているか,あるいは誤用しているかを把握することが肝要である.本研究は今日最も普及している送信元ドメイン認証技術のひとつであるSender Policy Framework(SPF)に着目し,同技術が実インターネットにおいてどのように利用,あるいは誤用されているかを詳細に分析した結果を報告する.本研究では正常なドメインとスパム送信に使われるドメインの両方を分析対象とし,中規模の企業において計測した電子メール配送ログおよび様々なIPレピュテーションリストを用いた相関分析を行う.はじめにSPFの普及と利用状況をグローバルおよびローカルな視点で分析した結果を示す.つぎにスパム送信者がスパム検出から逃れるためにどのようにSPFを活用しているかを示す.また,正常な送信者から送信された正常なメールの内,無視出来ない量のメッセージがSPFで指定されたポリシーによってリジェクトあるいはスパムの可能性があるメッセージとしてマークされることを示す.本研究の貢献は,メールサーバ管理者に対して送信元あるいは受信先としてのポリシーを正しく設計するための有用な知見を与えること,および研究者に対して電子メール送信元認証技術の有効性,限界,および必要となる改良を把握するにあたって有用な基礎データを示すことである.

近年,IPトラヒック計測手法として,フロー計測技術が注目されている.本稿では,本計測技術により収集されるフロー情報の,ネットワーク監視運用への適用に関する研究動向を紹介する.特に,ネットワークへのインパクトが大きいフローの特定,異常トラヒック検出,更に品質把握への応用に関して,著者らの研究内容を交えて紹介する.

筆者らは以前,ピーク時の配信サーバ負荷低減を目的に,ユニキャスト配信に加え配信要求とは無関係にコンテンツをブロードキャスト(BC)配信することを提案した.しかしSTBに大容量のストレージが必要となる.一方,BC事前配信コンテンツ数を抑制すること,またユーザをコンテンツ視聴履歴に基づきクラスタリングすることで,本問題に対処できる可能性がある.そこで本稿ではこれらの効果をVoDのアクセスログを用いて評価する.その結果,配信抑制により配信サーバ負荷低減効果の劣化を20%程度に抑えながらSTBの必要ストレージ容量を半減できること,またクラスタ数が2程度の時のみクラスタリングの効果が見られることを明らかにする.

近年,動画コンテンツを配信要求に応じてオンデマンドに配信するVoDサービスの普及が目覚ましい.VoDサービスにおいて,配信要求量は一日の周期で大きく変化するが,ピーク時の需要に対しても安定した品質でサービスを維持する必要があり,サービスプロバイダにとってピーク時のサーバ負荷低減が課題である.そのためには同一コンテンツを要求する複数のユーザに対してマルチキャスト配信を行うことが有効であるが,ユーザのレスポンス時間が増加する.またユーザ端末やSTBをキャッシュサーバとして活用するP2P型配信も有効であるが,自律的なユーザの行動にシステムの性能が依存し,全体最適化が困難などの問題がある.またこれらの方式では,ユーザのVCR操作に対応するために配信ツリーや配信元ピアを切り替えるなどの複雑な処理が必要となる.そこで本稿では,全ユーザにコンテンツを配信要求とは無関係に事前にブロードキャスト配信することで,ユーザのレスポンス時間を増大させることなく,サーバの負荷低減を図ることを提案する.また実際のVoDシステムにおけるアクセスログデータを用いた評価を行い,提案方式の有効性を明らかにする.

本研究は,YouTubeやニコニコ動画等に代表される大規模な映像共有サービスのワークロード分析を試みる.はじめに映像共有サービス発のトラヒックフローを検出するためのシンプルで効果的な方法を提案する.鍵となるアイディアは大規模なサーバー・ファームにおける命名則やアドレス割り当てにおける慣習を利用することにある.次に検出したフローを分析し,映像共有サービスのワークロード分析を行う.分析の結果,映像共有サービスにおけるフローサイズ分布に特有な性質を明らかにする.また,この性質は映像共有サービスにおける有料会員に対する差別化サービスの存在に起因することを示す.さらに,この性質が持たらす意味について考察を行う.

Toward scalable byte caching mechanism that operates on high-speed core routers, this paper proposes a selective packet filtration mechanism, using a probabilistic approach. We develop a fast approximation algorithm for determining whether the router should cache bytes in a probabilistic way. Theoretical and experimental analysis demonstrates that our technique achieves good enough approximation for both small and large selection probabilities, which can be used to perform accurate filtration, i.e., low false positive ratio or low false negative ratio.

2008年11月11日に実施されたホスティング会社McColo社の上位ISPによる遮断は,世界の至るところでスパム流量を半分あるいはそれ以上減少させるという画期的な効果をもたらした.2007年夏以降の世界的なスパム流量の爆発的増大を引き起こした主要因とされるスパムボット,Srizbiをはじめ,今日の主要なスパムボットのC&Cサーバが同社によってホスティングされていたことが明らかになっている.本研究では世界最大規模のスパムボットであるSrizbiの全体像把握,およびインターネットエッジサイトの視点からスパムボットC&Cを標的とした制御の有効性を定量化することを狙いとする.分析には2007年7月から2009年4月にかけて3箇所の異なるネットワークで計測された広範なデータを用いた.本研究の主要な貢献は次の通りである.(1)Srizbiの世界的な規模を確率的な方法によって推定した.(2)Srizbiボットネットから送信されたスパムの流量とそのインパクト,およびMcColo社遮断による効果をインターネットエッジサイトの視点から定量化した.(3)Srizbiの勃興と衰退,そしてMcColo社の遮断前後におけるバージョン遷移など,スパムボットの全体像把握に有用な発見を明らかにした.

高品質動画のビットレートは非常に高く,リンク輻輳の要因となることが懸念される.そのためリッチコンテンツのストリーミング配信に際しては,リンク使用率を平準化し,最大リンク使用率を低減することで,最大NWスループットを向上させることが重要である.そこで筆者らは以前,最大NWスループットの最大化を目的に,ISPがNW内に配置された複数のサーバから各配信セッションに対して並列配信を行うことを提案し,最適サーバ配置法とサーバ選択法を提案した.本稿では23の商用ISPのNWを用いた評価を行い,現実のNWにおいてリンク容量がどのように設計されているかを分析し,リンク重み設定が最大NWスループットに与える影響について明らかにする.そしてNWトポロジが,並列ストリーミング配信の効果に与える影響について分析する.

大規模VoDサービスの負荷を低減する有望な技術として,P4Pに代表される管理ピア協力型CDN(managed peer-assisted CDN)が注目されている.これらのアプローチは理論上,あるいは制御下にあるテストベッド網においてその有効性が証明されている.しかしながら実際のインターネット上においてピアが協調的な行動をとるインセンティブは一般に明らかではない.したがって,今後は実運用可能性の評価が問題になると考えられる.本研究ではこの点に着目し,ピア協力型CDNをより実用的にするための新たなビジネスモデルを提案する.鍵となるアイディアはエンドユーザがストレージ,帯域,CPUパワーといった余剰なリソースをISPに「販売する」ことを許容することであり,換言すればISPが陽なインセンティブをユーザに提示することによってユーザのリソースを柔軟に活用可能にし,結果としてシステムの効率性を向上するしくみである.シミュレーションにより,インセンティブ及びその他の外的要因がシステムの効率に与える影響を調べた結果を報告する.

P2Pトラヒックはインターネットにおける総トラヒック量の大きな部位を占めており,ISPにとってNW内を流れるP2Pトラヒック量の削減が大きな課題である.ISPがP2Pトラヒック量を削減する一つの方法として,一部のルータのポートにキャッシュ装置を設け,ピアから配信要求のあったコンテンツをキャッシュから配信することで配信フローの経由ホップ長を低減することが有効である.そこで著者らは以前,トランジットISPが下位のNWとの接続点にキャッシュを設置する場合を対象に,総P2Pトラヒック量を最小化する動的計画法を用いた最適キャッシュ設計法を提案した.本稿では実際の31のISPバックボーンNWに本最適設計法を適用し,P2Pトラヒック削減のためにキャッシュの設置が効果的なノードの特性,およびキャッシュが効果的となるトポロジ構造に関して明らかにする.

インターネット上での動画ストリーミング配信サービスの利用が増加している.動画コンテンツに対するユーザのニーズは高く,将来,HDTVやUHDVクラスの品質を有する超高精細コンテンツの動画ストリーミング配信が提供され,普及していくことが予想される.しかしこれら高品質動画のビットレートは非常に高く,リンク輻輳の要因となることが懸念される.インターネットの経路は静的なリンク重みを用いて決定されるため,特定のリンクに負荷が集中した場合,多数のリンクの使用率が低い状態であっても最大使用率となるボトルネックリンクの容量でネットワーク(NW)全体で達成可能なスループット(最大NWスループット)が決まる.そのためリッチコンテンツのストリーミング配信に際しては,リンク使用率を平準化し,最大リンク使用率を低減することで,最大NWスループットを向上させることが重要である.そこで本稿では最大NWスループットの最大化を目的に,ISPがNW内に配置された複数のサーバから各配信セッションに対して並列配信を行うことを提案し,そのための最適サーバ配置法とサーバ選択法を提案する.

Overlays and P2P technologies have evolved as vehicles to enable wide-area deployment of network services such as various kinds of content delivery services. On the other hand, disregarding underlay network topology, the current P2P applications may cause inefficient network resource utilization and poor application performance, which poses a significant problem as the rapid increase in the content delivery traffic. We thus investigate a way to optimize application traffic through cooperation between overlay and underlay networks to improve application performance as well as resource utilization in network providers.

バックボーンネットワークなどにおいて観測される多重化されたトラヒックには,昼夜変動や平日休日変動など周期的なトラヒック変動が見られる.筆者らはDDoS(Distributed Denial of Service)攻撃や設備故障によるトラヒック変動の検出を行う際に時系列の周期性を利用することで検出精度を向上させる手法を提案したが,周期性の有無の判定は目視による主観で行っていた.しかし,大規模ネットワークを対象としてスケーラブルにトラヒック変動検出を行うためには,観測時系列の周期性判定自体も自動化する必要がある.本稿では周期性判定の自動化を目指し,大規模ネットワークにおける複数のリンクから計測されたトラヒック量時系列データ(bps値)を対象とし,フーリエ変換を利用した手法による周期的トラヒック変動の分析結果を報告する.

P2Pトラヒックはインターネットにおける総トラヒック量の大きな部位を占めており,ISPにとってNW内を流れるP2Pトラヒック量の削減が大きな課題である.ISPがP2Pトラヒック量を削減する一つの方法として,一部のルータのポートにキャッシュ装置を設け,ピアから配信要求のあったコンテンツをキャッシュから配信することで配信フローの経由ホップ長を低減することが有効である.しかしキャッシュの効果はキャッシュを設置する場所とキャッシュの容量に大きく依存するが,これらキャッシュの設計法については十分な検討がなされていない.そこで本稿では,トランジットISPが下位のNWとの接続点にキャッシュを設置する場合を対象に,総P2Pトラヒック量を最小化する,動的計画法を用いた最適キャッシュ設計法を提案する.また31の商用ISPバックボーンNWに対して本設計法を適用した結果について示す.

スパムメールの急増に伴い,メール配送処理系の負荷集中が顕著な問題となっている.スパムメールに要する配送処理負荷を軽減する手段としてDNSBL(DNS BlackList)に代表されるIP reputationサービスが有効なメッセージ受信制限手段として広く利用されている.これらの手段はIPアドレスの参照のみで処理が可能なため,スケーラビリティに優れる手法である.一方,これらのアプローチには根源的な制限が存在する.すなわち,柔軟性,拡張性,局所性,そしてスパムと通常の双方を取り入れたモデルの欠如である.本論文はこれらの制限を統計的な手法によって解決する手段を提案する.その手法をPrBL(確率的ブラックリスト)と呼ぶことにする.主要なアイディアは送信元ホストの地理的位置,論理的位置,TCPヘッダのシグネチャ等のメッセージの中身と独立な固有な特徴量を利用することであり,機械学習の手法によって送信ホストを分類する.ある企業網で4ヶ月にわたって計測したSMTPログを用いて本方式の性能を評価した結果について報告する.また,PrBLは広く使われているDNSBLと比較して高精度なフィルタリングが可能であることを示す.

時々刻々と変化する負荷に対して柔軟で効率的な制御が可能なコンテンツ配信プラットフォームとして,ユーザのリソースを積極的に活用するP2P CDNが注目を集めている.P2P CDNの有効性は多くの研究者によって示されているが,いずれもピアに対する明確なインセンティブが存在しないこと,すなわち運用実現性がオープンプロブレムとなっている.本研究はネットワーク事業者が陽なインセンティブをユーザに提供するビジネスモデルがこの問題を解決できる可能性を探る.はじめにネットワーク事業者の立場からコスト・収益のバランスを最適化するインセンティブの設計フレームワークを論じる.つぎにビジネスモデルを実現するシステムアーキテクチャ概要を示し,考え得る様々な課題と解決策について議論する.

近年,インターネットにおける動画配信の急増が目覚ましく,今後,大容量リッチコンテンツ配信の普及が予想される.リッチコンテンツ配信の所要伝送帯域は大きく,ISPにとって消費NW資源量を最小化する効率的な配信システムの構築が急務である.リッチコンテンツを経済的かつ快適にユーザに提供するためには,ISPが自ら大容量のサーバ拠点を自身のNWの限定された箇所に最適配置し,集中的にサーバを最適選択するISP型CDNが有効であり,筆者らはISP型CDNに適したコンテンツ展開法,配信処理法,サーバ拠点配置法について検討した.本稿では,コンテンツの拡散特性,平均ホップ長の推定精度,31のISPのNWトポロジにおける総トラヒック量の削減効果について評価し,ISP型CDNの有効性について検証する.

近年,インターネットにおける動画配信の急増が目覚ましく,今後,大容量リッチコンテンツ配信の普及が予想される.リッチコンテンツ配信の所要伝送帯域は大きく,ISPにとって消費NW資源量を最小化する効率的な配信システムの構築が急務である.従来,Webコンテンツ等を効率的に配信する方法としてCDNが広く用いられている.しかしCDNでは,CDN事業者が複数のISPのNW上に多数のサーバを設置し,計測により得られた不完全な情報を元にサーバを選択するため,ISPにとって最適な配信形態が実現されない.さらに多数の拠点にサーバを設置するため各サーバの大容量化がコスト的に困難であり,リッチコンテンツの配信には適していない.また近年,P2P型コンテンツ配信の試みも見られるが,配信サーバの機能が利己的に振舞うユーザに依存しており,やはりISPにとって最適な配信形態が実現されない.リッチコンテンツを経済的かつ快適にユーザに提供するためには,ISPが自ら大容量のサーバ拠点を自身のNWの限定された箇所に最適配置し,集中的にサーバを最適選択することが有効である.そこで本稿では,このようなISP型fDNに適したコンテンツ展開法,配信処理法,サーバ拠点配置法について検討する.

複数配信木を用いたアプリケーションレベルマルチキャストは,マルチメディアコンテンツを送信する方式として,遅延や帯域利用効率の面で有効である.しかし複数配信木方式は,ノードの故障が生じた時に,単一配信木と同様に再構築が必要であり,再構築完了までの品質の劣化を避ける手段を有していない.本稿では,従来個々に独立して動作していた各配信木間で,障害発生時でも十分な品質を確保するために,プロアクティブなランダム転送を行うことで障害の影響を軽減し,さらに障害検知時に,一時的に木の構造を変化させ他の配信木からの転送を行う方式を提案する.これにより木構造の配信木方式の利点である,低遅延と管理の容易さを維持したまま,障害発生時の影響を軽減し高い接続性を維持することが可能となる.本提案方式の有効性を計算機シミュレーションにより示す.

インターネット上においてネットワークリソースの浪費や品質劣化を引き起こす異常トラヒックを検知・制御する技術は,安心で快適な通信サービスを提供するために不可欠となっている.本稿では,異常トラヒックを検出するためのトラヒック測定分析手法について,関連研究動向の紹介を交えながら筆者らの研究内容について紹介する.また,各手法の実データ評価結果も示す.

近年,ワーム感染ホストが生成する異常トラヒックが,ネットワークに深刻な被害をもたらすことが問題となっている.そこで筆者らは以前,ワーム感染ホストは短時間に大量のフローを生成することに着目し,フローサンプル情報からワーム感染の疑いの高いホストを特定する方式を提案した.本方式は,実装メモリ量Bと,任意に定めた三つのパラメタ(測定周期φ,φ内の生成フロー数mに関する特定閾値m^*,m=^*のホストが特定される確立H^*)に対して,m≧m^*のホストの特定精度を最大化するよう他のパラメタを自動的に最適設計する.しかし,三つのパラメタφ,m^*,H^*をどのように設定するかが未解決である.そこで本稿では,新たにワームの出現を検知してから,そのワームに対するパッチやワクチンが開発されるまでに要する時間T内において,アクティブなワーム感染ホスト数の,そのワームに対して脆弱な全ホスト数に対する比率を,任意に定めた上限値以下に抑えることを目的に,これら三つのパラメタを設計する方法を提案する.

近年,インターネットではワームの感染拡大が問題になっている.ワームに感染したホストは次に感染させるホストを見つけるためにスキャンを行うため,短時間に大量のフローを生成する特徴がある.そこで筆者らは,任意に定めた測定期間と閾値に対して,測定期間内の生成フロー数が閾値以上のホストをSuperspreaderと定義し,サンプルフロー情報を用いてSuperspreaderを特定する方法を提案した.しかしSuperspreaderにはDNSサーバといった,正常なホストであるが大量のフローを生成するホストも含まれる.そのため単に特定されたSuperspreaderに対してレート規制や切断といった規制処理を行うと,正常なホストまで規制される問題がある.一方,正常なホストで大量のフローを生成するホストは,連続した複数の測定期間でSuperspreaderとなる傾向がある.そこで本稿では,正常期間中には特定されたホストのIPアドレスをホワイトリストに収容し,ワーム感染拡大中は,特定されたホストをホワイトリストと照合することで,ワーム感染ホストを絞り込む方法を提案する.

本稿では,サンプリングによる異常検出精度の劣化について定量的な評価を行った.ランダムパケットサンプリング,通常トラヒックの分布を仮定した上で,False Positive Ratio(FNR), False Negative Ratio(FNR)の計算式を与え,同式を用いて,与えられたFNR,FPRを達成可能となる攻撃トラヒック量やサンプリングレートを導出した.導出式の精度について,実トラヒックデータを用いて評価を行った.また,サンプリングレート,FNR,FPR,検出すべき攻撃トラヒック量が与えられている場合に,それらを達成するために監視粒度を変化させる手法の検討も行った.

著者らはこれまでに,ネットワークスキャンやSYN floodingのような小さなフローを大量生成する異常トラヒックはパケットサンプリングを行うと検出困難になることを実データを用いて示した.これは,このような異常フローが,正常フローに比べてパケットサンプリング時にサンプルされにくいことに起因する.本稿では,パケットサンプリングが異常検出精度へどのような影響を与えるかを定量的に分析する.その際,フローサンプリング時の異常検出精度とも比較する.また,パケットサンプリング後も異常トラヒックを適切に検出可能とするような,トラヒックの分割監視方法およびその分割数決定法についても述べる.

ネットワークトラヒック監視において,DDoS攻撃や輻輳等の異常トラヒックの発生をリアルタイムかつ自動的に判定する技術を提案し,実ネットワークで計測されたトラヒックを用いて提案技術の有効性を検証する.特に,昼夜変動や週変動といった周期性を示すトラヒックの監視に焦点を当て,異常判定において周期性を考慮することで判定精度が向上することを示す.本手法は,平常時と統計的に異なる性質を持ったボリューム変化を異常トラヒックとして判定する一方で,オペレーショントラヒックのような周期的に発生する巨大トラヒックは正常として学習する.そのため,周期を考慮しない従来の手法よりも誤検出を少なく抑えることを実現した.

DDoS(Distributed Denial of Service)攻撃に代表される異常トラヒックを検知・制御する技術は,近年その重要性が増してきている.スケーラブルなネットワーク監視のためには,異常検知精度の向上と検知技術運用時の手間を抑えることが要求される.また,異常継続状況や異常の終了を正確に判定することも必要となる.これは異常検出,終了判定が,その後の原因分析やトラヒック制御の開始・終了のトリガとなるためである.そこで,本研究では,異常の検出と終了のリアルタイム判定を自動化する異常トラヒック検知手法を提案し,その有効性を検証する.

パケットサンプリングを通じて得られるフロー情報を用いて,異常トラヒックを検出する方法について報告する.まず,ネットワークスキャンやSYN floodingのような,小さなフローを大量発生する異常トラヒックはパケットサンプリングすると検出が困難になることを示す.これは,正常フローに比べてこのような異常フローは,個々のフローを構成するパケット数が少ないため,サンプルされにくいことに起因する.次に,この問題への解決法として,監視トラヒックを空間方向に分割して複数グループに分け,各グループのトラヒックについて時系列解析を行うことにより,パケットサンプリング後も異常トラヒックを適切に検出可能であることを実データ分析を通じて示す.

パケットサンプリングを通じて得られるフロー情報を用いて,異常トラヒックを検出する方法について報告する.まず,ネットワークスキャンやSYN floodingのような,小さなフローを大量発生する異常トラヒックはパケットサンプリングすると検出が困難になることを示す.これは,正常フローに比べてこのような異常フローは,個々のフローを構成するパケット数が少ないため,サンプルされにくいことに起因する.次に,この間題への解決法として,監視トラヒックを空間方向に分割して複数グループに分け,各グループのトラヒックについて時系列解析を行うことにより,パケットサンプリング後も異常トラヒックを適切に検出可能であることを実データ分析を通じて示す.

パケットサンプリング技術は,フロー統計をスケーラブルに計測する技術として欠かせないものになってきている.実際,今日のISPの多くはサンプルフロー統計をベースにネットワークを監視している.ところで,近年の研究によればフロー統計からトラヒックパターンを分析することによって,ネットワークに生じる異常を検出できることが明らかになっている.たとえばサイズの小さなフローの数が急激に増加したら,それは大規模なワームの発生に関連している可能性がある.しかしながらパケットサンプリングから得られるフロー統計を分析する場合,そのようなパターンを正しく捉えられない可能性がある.なぜならサンプリングによって小さなフローの大多数がサンプルされないからである.本研究では,実計測データを用いサンプリングによって元のフロー統計がどの程度情報を失うかを示す.つぎに不完全データに対する最尤推定方法であるEMアルゴリズムを用いても,元のフロー統計を正しく推定できないことを実験例を用いて示す.最後に元のフロー統計に関する追加の情報(非サンプルフロー数)を導入することによって,元のフロー統計の推定精度が著しく向上し,結果としてサンプルフロー統計から元のトラヒックパターンの変化を捉えることが出来ることを示す.

多くのユーザに同一データを配信する場合,帯域の効率的利用の面からマルチキャストが有効である.しかし,マルチキャスト環境においては,グループアドレスを知るものは,誰でもグループ内にデータを送信できるため,ソースの「なりすまし」が行われる危険性がある.これを防ぐためには,送信者認証を行う必要がある.一方,ネットワーク上でのロスに対処するために,データの復元処理は必要不可欠である.これらは共に必須の技術であるのに対し,従来の既存方式ではそれらはそれぞれ独立な処理として開発が進められてきた.そこで本稿では,上記の技術の融合による性能の効率化を目的とし,マルチキャスト環境における,FECを用いた送信者認証とデータ復元の連携手法を提案,評価する.Department of Information Networking, Graduate School of Information Science and Technology, Osaka University

インターネットにおいてワームの拡散が問題になっている.ワームの拡散を抑えるためには,ワームの出現に対して早期にワーム感染ホストを特定し,ネットワークから切り離すなどの対処をとることが重要となる.ワーム感染ホストは,ポートスキャンのために短時間に大量のフローを生成する特徴がある.そこで筆者らは,スケーラビリティを実現するためにフローサンプルを用いた大量フロー生成ホストの特定方式を提案した.ホストごとにフローの異なり数を計測する必要があるが,提案方式は新規フローの判定にBloom filterを用いる.また,実装メモリ量を各処理モジュールに最適配分する方法を示したが,大量フロー生成ホストの生成フロー数の中央値を適切に推定する必要がある.本稿では,中央値を適切に推定する方法を提案する.また,提案特定方式の有効性を明らかにするために,他方式との比較を含めた性能評価結果についても示す.

大規模トラヒックデータの詳細かつ長期間にわたる分析・蓄積を可能にするネットワーク計測技術,NetDelta (DEtailed, Long-Term Analysis)を提案する.中心となるアイディアは,ハッシュ関数およびビットマップを用いた複数の異なり数計数法を適切に組み合わせることによって,幅広い範囲の異なり数を指定した推定精度で推定することである.各々のビットマップのサイズは小さいため,長期間にわたる計測が可能である.同手法を用いることによって分析トラヒックデータの時間的および空間的な集約が可能になる.超高速バックボーン回線で計測した実トラヒックデータを用いた評価結果についても報告する.

ワーム感染ホストは短時間に大量のフローを生成することから,大量フロー生成ホスト(フローHog)を早急に特定することが重要となる.そこで筆者らは以前,フローサンプルを用いてフローHogを特定する方式を提案した.本フローHog特定方式は,新規フローの判定を行うBloom filterと,サンプルフロー数をホストごとに集計するホストテーブルとで構成される.本稿では,フローHogを見逃す確率を最小化するよう,与えられたメモリをBloom filterとホストテーブル間で最適配分する方法を提案する.

本稿では,長時間帯域占有トラヒックを生成するフローを適切に制御し,レスポンス時間に敏感なwebのような小さいサイズのフローの品質を確保できるQoS制御方式について述べる.既存のQoS制御方式の多くは,持続時間に依らずにその瞬間の各フローレートが公平になるように制御する.しかし,この方法では長時間フローにも常に一定の帯域を与えるため,その間,小さいサイズのフローへの割当帯域が制限されてしまう.それに対し,著者らはこれまでに,持続時間も考慮してフローレートを制御することにより小さいフローの品質を向上させるQoS制御方式を提案した.本稿では,提案方式でのパラメータ設定法について説明し,提案方式の有効性をシミュレーション評価を通じて示す.また,スケーラビリティ向上のためのパケットサンプリング技術の適用方法についても議論する.

ネットワーク管理者にとって,管理網の配下に存在するホストの情報を把握することは有益である.ホスト毎に監視する統計値として,パケット数,バイト数などの累積値はインクリメンタルな計数が容易である.しかしながら,異なるあて先ホスト数,異なる着ポート番号の総数などの異なり数は容易ではない.本研究は異なり数を含めたホスト毎の統計情報集約方法-NetHost-を提案する.中心となるアイディアは,定数長のビットマップ用いた確率的な異なり数計数方法を適用すること,および複数に分割したトラヒックサマリを集約することである.提案方法を実装し,実トラヒッタデータを用いて評価した結果についても述べる.

近年,ワーム,ウィルス,DDoS等が引き起こす異常トラヒックが,ネットワークに深刻な被害をもたらすことが問題となっている.DDoSの踏み台にされたホストや,ワームやウィルスに感染したホストは,個々のフローサイズは小さいものの短時間に大量のフローを生成するという特徴がある.そのため,短時間に大量のフローを生成するホスト(フローHog)を早急に特定し,ネットワークから切り離すといった対処をとることが重要となる.本稿では,フローサンプリングによって得られた情報からフローHogを高精度に特定する方式を提案する.

本稿では,フロー数、通信相手ホスト数等の異なり数上位ホストを小サイズメモリで推定する手法を提案する.DDoS、スキャン等の異常トラヒックを検出するためには、パケット数,バイト数といったトラヒック量だけでなく,ホスト毎のフロー数、通信相手ホスト数等の異なり数を監視することが有効である。しかしパケットストリームデータからホスト毎にこれら異なり数を算出するためには,ホスト毎の既出情報テーブル(e.g.フローテーブル)を管理する必要があるため,必要メモリ量が膨大になる.本稿ではこれらホスト毎の異なり数の算出において,算出対象を上位Nホストのみに限定することで、少ないメモリ量で推定する方式を提案する.また本方式の異常検出への応用についても述べる.

フローを対象としたトラヒック計測においては, 通常, 管理すべきフロー数を抑える目的からパケットサンプリングが用いられる. この際, フローごとに各種統計値をフローテーブル(FT)にて管理するが, 一定時間(タイムアウト時間), 新たにパケットがサンプルされなかったフローを終了したものと見なしFTより消去する. タイムアウト処理として, 従来は一定周期ごとにFT内の全エントリに対してタイムアウトの有無を調べる方法が用いられているが, 扱うべきフロー数の増加に伴い, タイムアウトの処理に要する時間が問題となる. 本稿では, 一定周期ごとに, ランダムに選んだV個のエントリに対してのみタイムアウトの有無を調べることを提案する. 提案方式は所要メモリ量の増加を抑えながら, メモリアクセス数を大幅に低減することが可能であり, 例えば1割程度のメモリ増加で最大メモリアクセス数を1/1000以下に低減することが可能となる.

ホスト毎の通信パターンを分析することによりワーム感染の疑いが強いホストを検出する方法を提案する.本研究のアプローチは以下の2つのステップによって構成される.はじめに通信パターンの特徴量を定量的に定義する.上記の特徴量を用いることにより, ワーム感染ホストが固有のパターンを有すること, およびそれらが他の通信パターンに対して弁別可能であることをクラスタ分析によって明らかにする.つぎに, 通信パターンに対してナイーブベイズ分類器(NBC)を適用することにより, ワーム感染ホストを検出する方法を提案する.実計測したトラヒックデータを用いて提案手法の性能を評価した結果についても報告する.

ホスト毎の通信パターンを分析することによりワーム感染の疑いが強いホストを検出する方法を提案する.本研究のアプローチは以下の2つのステップによって構成される.はじめに通信パターンの特徴量を定量的に定義する.上記の特徴量を用いることにより, ワーム感染ホストが固有のパターンを有すること, およびそれらが他の通信パターンに対して弁別可能であることをクラスタ分析によって明らかにする.つぎに, 通信パターンに対してナイーブベイズ分類器(NBC)を適用することにより, ワーム感染ホストを検出する方法を提案する.実計測したトラヒックデータを用いて提案手法の性能を評価した結果についても報告する.

サンプルパケット情報を用いたTCPフローレート推定法について述べる.TCPヘッダ内のシーケンス番号を用いることにより, 高精度なフローレート推定が可能なことを示す.また, 提案方式におけるパラメータ(e.g., サンプリング確率)設定方法について分析し, サンプリング確率が小さい場合に推定精度が向上することを示す.さらに, 提案方式を用いてTCPフロー品質劣化検出が可能であることを実データを用いて示す.

ウェブの次代を担う技術として, P2Pや分散コンピューティングを代表とするインターネット上でオーバレイネットワークを構成する自律分散型システムの普及が目覚ましい. このような大規模システム上ではその性質上, アプリケーションの内部プロトコルを変更することなしに, その全体の特性を測定することは困難である. 本論文では, P2Pアプリケーションに汎用的に適用可能な測定法と, 一部にその測定法を適用し得られた測定結果から, P2Pネットワーク全体の挙動や規模の推定を行うための汎用的な手法を提案する. 更に実P2Pネットワークに本手法を適用し, その有効性を具体例で示す.

ネットワークの計測に基づき, ワーム感染の疑いが強いホストを検出する方法を提案する.判定手法には, スパムフィルタリング等に応用されているナイーブベイズ分類器(NBC)を用いる.NBCは事前にホストが持つ属性(例えば通信パターン)とホストが属するクラス(例えばワームに感染しているか否か)の関係を学習し, 新たに計測したホストの属性値情報からそのホストが所属するクラスを確率的に判定する.事前データには存在しない属性を持つホストに対しても判定を下すことができるため, ロバスト性に優れる手法である.実計測したデータを用いて提案手法の性能を評価した結果についても報告する.

筆者は以前, パケットサンプリングによって得られる情報から高レートフローを特定する方式, Short timeout法を提案した.Short timeout法ではタイムアウトせずに2個のパケットがサンプルされたフローを特定していたが, 本稿ではこのことを一般化し, タイムアウトせずにY個のパケットがサンプルされたフローを特定する場合について検討する.そして任意のフローレートに対する特定確率を導出し, 数値評価により, Yの増加に伴い所要メモリ量と処理負荷が増大する反面, 特定精度が向上することを明らかにする.

ユーザやネットワークが発生するトラフィックの単位である「フロー」を計測することにより, ネットワーク性能劣化の原因解明や異常トラフィックの特定が可能となる. すなわち, 問題を引き起こす可能性のある異常フローがネットワークに到着したことを迅速に知ることができれば、適切な制御やトラブルシューティングを速やかに実施出来る. 本研究はナイーブベイズ分類器を用いたフロー特性分類方法を提案する. 提案手法は過去に学習したフローの属性とフローが属するクラスの確率的な関係をもとに, 新たに到着したフローの属性からそのフローが所属するクラスを確率的に判定する. 提案方法はフロー計測によって得たすべてのフローテーブルを管理する必要がないことから, スケーラビリティにも優れる. 実計測したデータを用いて提案手法の性能を評価した結果についても報告する.

回線速度の高速化に対してスケーラブルなトラヒック測定を可能とする技術としてパケットサンプリングが注目されている.本稿では, N個に1個のパケットを抽出する通常のパケットサンプリングによりサンプルされるフローの挙動から, 元のフロー全体のTCPフローレベル品質劣化を検出する方法について述ベ, その検出精度評価結果を報告する.また, フローサイズやフローレート, フロー持続時間といったTCPフロー情報に関する分布を推定するためのサンプリング法も提案する.提案方法は, 各TCPフローのSYNパケットはフローサイズに依らず等確率で抽出されるという性質を利用する.さらに, 提案方式の有効性を実測データ分析を通じて検証する.

本稿では, 超高速ネットワークにおけるトラヒック測定分析技術として, サンプルパケットのみを用いてトラヒック制御・品質管理に有用な統計情報を推定する手法を二つ紹介する.まず, 回線帯域の占有率が大きいユーザを特定する手法について述べる.本手法は, 帯域占有率の大きいユーザが他の一般ユーザの通信を圧迫している場合に, そのようなユーザを迅速に切り分けて制御することを可能とする.次に, パケットサンプリングにより抽出されたユーザのみの挙動から元のユーザ全体の品質劣化を検出する方法について述べる.また, 実測データ分析を通じて各方式の有効性を検証した結果についても報告する.

本稿では,IPネットワークの上位の論理ネットワーク(オーバーレイネットワーク)を利用したインターネットスケールのトラヒック制御を提案する.そのキー技術は,リアルタイム測定にもとづくIPレイヤの状態推定,その情報にもとづく動的トポロジ制御とQoS(Quality of Service)ルーチングである.本制御により,複数ISP(Internet Service Provider)をまたがるエンド・ツー・エンドでのユーザ間通信の高品質化やIPトラヒックの負荷分散が可能になると期待できる.

比較的流量の多いインターネット回線では,極めて多数のパケットから構成される少数のフロー(エレファントフロー)が全体のトラフィック流量の大部分を占める現象が観測される.エレファントフローを特定し,それを制御対象とすることにより,効率的かつ効果的なトラフィック制御を実現できる.一方,近年のネットワーク回線の高速化にともない,スケーラブルなネットワーク計測・管理技術としてパケットサンプリングが注目されている.しかしながら,パケットサンプリングを用いることにより,エレファントフローの特定はより難しいものとなる.本研究は,周期的にサンプルしたパケットからエレファントフローを特定する方法を提案する.提案方法の核となるアイディアは,ベイズの定理を用いることにある.これにより,false positive(誤検出)とfalse negative(未検出)の間に存在するトレードオフを柔軟に設定することが可能となる.また,実ネットワークのトレースデータを用いて,提案方法の評価を行った結果を報告する.

回線速度の高速化に対してスケーラブルなトラヒック測定を可能とする技術としてバケットサンプリングが注目されている.本橋では,パケットサンプリングにより抽出されるフローの統計情報から,TCPフローレベルの性能劣化を検出する方法を提案する.提案手法は,(I)パケットサンプリングにおいて抽出されるフローは高レートフローになりやすい傾向にあり,(ii)リンク輻輳時にはレートの高いフローから先に性能劣化が生じる,という2つの特性を利用している.また実測データ分析を通じて,提案方式の有効性を検証した結果についても報告する.

近年,P2Pアプリケーションの普及が目覚ましく,現在のインターネットトラヒックに占めるP2Pの割合はwebに匹敵するまでに至っている.本研究ではネットワークの計測を行い,web及びP2Pトラヒックのフロー分析を行った.特に,フロー到着間隔,フロー持続時間,フローサイズ,フローレートについて, web,P2Pそれぞれの特性を明らかにした.また,分析結果に基づき,P2Pフローの増加がトラヒック全体の特性に与える影響について考察した.本研究の結果は,アプリケーションのトラヒック特性を考慮したネットワークの設計,制御手法の提案及びトラヒックのモデル化に有益である.

比較的流量の多いインターネット回線では,極めて多数のパケットから構成される少数のフロー(エレファントフロー)が全体のトラフィック流量の大部分を占める現象が観測される.エレファントフローを特定し,それを制御対象とすることにより,効率的かつ効果的なトラフィック制御を実現できる.一方,近年のネットワーク回線の高速化にともない,スケーラブルなネットワーク計測・管理技術としてパケットサンプリングが注目されている.本研究は,特にエレファントフローに着目し,サンプルしたパケットからエレファントフローを特定する手法を提案する。また,提案手法を実データを用いて評価した結果を示す.

様々な転送速度を持つTCPフローを集約するリンクにおけるTCP品質推定法を提案する.アクセス回線速度が均一な場合のTCP平均ファイル転送時間はprocessor-sharingモデルで評価できることが知られている.そこで本稿では,まず,均一モデルの結果を元にアクセス回線速度が不均一な場合のTCPファイル転送時間近似式を導く.次に,着目する集約リンク使用率が十分小さいときのファイル転送速度を仮想アクセス回線速度としてその近似式に適用することにより,アクセス回線以外の転送速度制限要因も考慮してTCPフローの品質を推定する方法を提案する.また,その推定法に基づいた帯域設計管理法についても述べる.

インターネットヘのアクセス環境の向上により,P2Pアプリケーションの普及が著しい.特に,ファイル共有アプリケーションの普及は,著作権ビジネスに関わる者達をはじめとした様々な領域にその影響をおよぼしつつある.一方で,ファイル転送時にはサーバを介さないP2Pアプリケーションの特性から,大規模な情報収集は困難であった本稿では.P2Pファイル共有の規模,共有されるファイルの実態を明らかにするため, WinMX, Gnutella. Winnyについて,ヒューリステイックな測定手法に基づき,測定とコンテンツ分析を実施した.

インターネットヘのアクセス環境の向上により,P2Pアプリケーションの普及が著しく,ネットワークに多大な影響を及ぼしつつある.本稿ではP2Pアプリケーションの中でも最もネットワークヘの影響が大きいP2Pファイル共有アプリケーションについてその実態を明らかにするため,WinMX, Gnutella, Winnyについて,ネットワークレイヤとアプリケーションレイヤ両面からの測定と分析を実施し,トラヒック測定,トラヒック制御,ネットワーク設計/管理についてそれぞれ今後の課題を示した.

近年ネットワークトラフィックのゆらぎが持つ周辺分布は非正規性を示し、より現実に近いトラフィックモデルを構築する上で重要な特性であることが数多く報告されている。本研究では周辺分布が非正規性を示す要因を調べるために、単位時間ブロック内のフロー(Per-time-blockフロー)を定義し、各フローの持つ、フローサイズ、ノード間ホップカウント、アプリケーション種別とその相関を調べた。

ネットワークトラフィックのゆらぎが示す周辺分布の差異を生み出す原因を明らかにするために、IPフロー統計の解析を詳細に行った。この結果我々は(1)平均アクティブフロー数の増加により必ずしも周辺分布が正規分布へ収束するわけではないこと、(2)時間ブロック毎のフローサイズ分布がパワー則に従うこと、および(3)このフロー構造が集約トラフィックの周辺分布特性と相関を示し、ネットワークトラフィックのモデル化や品質評価に重要な意味をもつことを明らかにした。

ネットワーク社会の最大の脅威となっているのがサイバー攻撃である．攻撃対策技術を確立することが社会的な要請であるが，従来の対策技術は個々の攻撃に対処するものであり，しかも人間の介在が必要であった．
本研究は，データ科学からのアプローチを採り，サイバー攻撃対策技術の研究成果を有効に蓄積するためのマルウェア・インフォマティックスの確立を目指した．
本研究では，実際に大量のデータを蓄積した．さらにデータの特徴を抽出・選択する際の素性(feature)エンジニアリングの技法を体系的に整理して比較した．データの特徴を使う対策技術において中心的な役割を担う機械学習のアルゴリズムを評価する方法を提案した．