Configuring security and privacy (S&P) settings can be challenging for non-expert users, resulting in excessive dependence on persuasive cues, such as social proofs or expert suggestions. Although such suggestions can promote protective user choices, they can be misused as deceptive patterns that steer users toward less-protective settings. This study examines (1) how source-based suggestions (public vs. experts), when combined with logical persuasive statements, influence decision-making in S&P settings under honest or deceptive conditions and (2) how users evaluate these approaches once deception is revealed. An online experiment with 1,433 U.S. participants utilizing a 2 × 2 × 2 factorial design revealed that persuasive statements amplified the effect of social proof- and authority-based cues, which persisted even when promoting less-protective settings. These findings demonstrate the importance of persuasive S&P interfaces that follow transparent and rational design, as well as complementary interventions that foster users’ critical assessment and resilience against manipulation.

Secure development guidelines contribute to improving software security from the development stage by making developers aware of the risks to be assumed, the necessary security countermeasures, and how to implement them. In this study, we investigated the actual utilization of guidelines and their usability in the industry through a survey of software development professionals in the U.S. and Japan (N=396 in the U.S. and N=474 in Japan). Our quantitative analysis revealed that “in-house” guidelines not examined in most existing studies are in fact widely utilized in the industry and also clarified how they are related to the use of public guidelines. In addition, we found that the practices for implementing guidelines recommended by existing studies are difficult for software development professionals with certain attributes, e.g., those who are working on small projects. The findings demonstrate the need for lightweight recommended practices taking into account organizational issues at industrial development sites that are easy for developers to implement.

Attacks on Internet of Things (IoT) devices have become increasingly sophisticated. However, there exist few comprehensive security investigations of IoT devices. We conducted a large-scale systematic investigation by assessing IoT firmware and follow-up survey with professionals involved in IoT-device manufacturing to understand the factors that prevent software security of IoT devices. Consequently, we discovered that many IoT devices continue to use old processor architecture and operating systems that are unable to efficiently use existing attack-mitigation features. Furthermore, we demonstrated that software patches are sometimes implicitly applied without changing the software version number (implicit patching); this may generate false positives in existing vulnerability assessments relying on software versions. On the basis of a follow-up survey, we determined technical and contractual constraints to IoT security emanating from the supply chain in the IoT device manufacturing industry. Based on the results, we discuss challenges associated with secure IoT manufacturing in the IoT-device supply chain.

Online social networks (OSNs) are threatened by Sybil attacks, which create fake accounts (also called Sybils) on OSNs and use them for various malicious activities. Therefore, Sybil detection is a fundamental task for OSN security. Most existing Sybil detection methods are based on the graph structure of OSNs, and various methods have been proposed recently. However, although almost all methods have been compared experimentally in terms of detection performance and noise robustness, theoretical understanding of them is still lacking. In this study, we show that existing graph-based Sybil detection methods can be interpreted in a unified framework of low-pass filtering. This framework enables us to theoretically compare and analyze each method from two perspectives: filter kernel properties and the spectrum of shift matrices. Our analysis reveals that the detection performance of each method depends on the effectiveness of the low-pass filtering. Furthermore, on the basis of the analysis, we propose a novel Sybil detection method called SybilHeat. Numerical experiments on synthetic graphs and real social networks demonstrate that SybilHeat performs consistently well on graphs with various structural properties. This study lays a theoretical foundation for graph-based Sybil detection and leads to a better understanding of Sybil detection methods.

Many studies have been conducted to detect various malicious activities in cyberspace using classifiers built by machine learning. However, it is natural for any classifier to make mistakes, and hence, human verification is necessary. One method to address this issue is eXplainable AI (XAI), which provides a reason for the classification result. However, when the number of classification results to be verified is large, it is not realistic to check the output of the XAI for all cases. In addition, it is sometimes difficult to interpret the output of XAI. In this study, we propose a machine learning model called classification verifier that verifies the classification results by using the output of XAI as a feature and raises objections when there is doubt about the reliability of the classification results. The results of experiments on malicious website detection and malware detection show that the proposed classification verifier can efficiently identify misclassified malicious activities.

A voice assistant (VA) is a platform that provides users with a wide range of services via interaction with a voice application using verbal commands. Since the VA application is deployed in the cloud, its behavior is not transparent to the user, which raises privacy concerns. In this study, we developed a framework called ChatterBox, which attempts to analyze VA applications via extensive continuous interaction, to understand their behavior. ChatterBox is capable of parsing and generating dialogues by utilizing natural language processing approach. It can also parse application-level messages to understand how a VA app acquires personal information. ChatterBox supports English and Japanese, which are completely different languages, and can extract more than twice as many dialogues from VA applications compared to SkillExplorer, a state-of-the-art VA dialogue analysis system. Based on analyses of English and Japanese VA applications using ChatterBox, we revealed that 5–15% of VA applications collect personal information or recorded user identifiers in a non-transparent manner, and 76–94% applications collected personal information without providing appropriate privacy policies. In light of these findings, we discuss the implementation of a highly transparent VA application platform.

Digital contact-tracing (DCT) applications have been installed on more than 188 M smartphones worldwide as an effective mechanism for monitoring contact with COVID-19 infected individuals. DCT is promising not only for COVID-19, but also for preparing for a possible future large-scale pandemic. The DCT framework is unique in that it combines Bluetooth Low Energy (BLE) communications with cryptography techniques to track exposure on a large scale while protecting user privacy. The objective of this study is to assess the risk of the linking attack to the DCT frameworks; i.e., linking individuals to the identifiers contained in BLE broadcast frames that are supposed to be anonymized. Specifically, we target Google/Apple’s Exposure Notification (GAEN), which is the representative implementation of DCT. Our extensive experiments demonstrate that passively collected rolling proximity identifiers (RPIs) contained in the BLE frames can be linked to face photos which could lead to the exposure of privacy information with high accuracy, including infection status. We also demonstrate that an attacker with a few number of devices can correctly link RPIs and the images of the target person with a success rate of 86% at a rate of 5,000 users per hour. Based on these results, we propose countermeasures to reduce the inherent privacy risk of the GAEN framework.

Software security, which is often regarded as a non-functional requirement, tends to be less prioritized than other explicit requirements in development projects. For designing security measures that can be used in software development, we must understand the obstacles that prevent the adoption of secure software development practices. In this study, we quantitatively analyzed security efforts and constraints of software development projects through an online survey of software development professionals in the US and Japan (N=664). We revealed how certain characteristics of a development project, such as the project’s contractual relationships or the software’s target users, influence security efforts and constraints. In addition, by comparing the survey results of two groups (developers and managers), we revealed how the gap in their security efforts and constraints influences software security. We believe the results provide insights toward designing usable measures to assist security-related decision-making in software development and conducting appropriate surveys targeting software development professionals.

With the spread of service platforms that enable users to generate content, people use user-generated content (UGC) to search for and access information on the web instead of search engines. Attackers can also use UGC on a service platform (UGC platform) to spread web-based social engineering (SE) attacks to a large number of people. In this paper, we focus on a type of web-based SE attack, called an event-synced navigation attack, which generates UGC with links navigating users to malicious websites and distribute it synced with a real-life event at a specific time. To understand the attacks in the wild, we propose a new system for detecting event-synced navigation attacks in real time by capturing the inevitable footprints left by attacks that affect a large number of users. We evaluate each of the three steps of the proposed system and finally find that the system can classify malicious and non-malicious UGC with 97% accuracy. Furthermore, we perform a comprehensive measurement study on event-synced navigation attacks spread from popular UGC platforms (Twitter, Facebook, YouTube, and Reddit) and confirm that many event-synced navigation attacks are deployed in the wild.

Android malware has been a growing threat. For an effective countermeasure against Android malware, we need to not only detect the malware at a certain point in time but also analyze its time-series changes of malware, taking into account that the family of Android malware will increase in number over time. In this paper, we propose a new method for automatically creating a "family tree" of Android malware that can represent how the newly detected Android malware is related to existing Android malware and its families, and how they have changed over time. Our evaluation using 24,474 actual Android malware APKs shows that our proposed family tree is able to accurately represent time-series changes between malware families.

In this paper, we propose a method for detecting malware-infected hosts with a high rate of detection and a low rate of false positives without using any data on benign communication. Based on the fact that many malware-infected hosts generate multiple HTTP requests, we propose a method using the templates of sets of those HTTP requests. For each malware, this method generates a template that comprises the set of templates of the HTTP requests that the malware generates. We call the set of templates group template. It then detects malware-infected hosts by comparing the set of monitored HTTP requests with the group templates.

By exploiting people's psychological vulnerabilities, modern web-based social engineering (SE) attacks manipulate victims to download malware and expose personal information. To effectively lure users, some SE attacks constitute a sequence of web pages starting from a landing page and require browser interactions at each web page, which we call multi-step SE attacks. Also, different browser interactions executed on a web page often branch to multiple sequences to redirect users to different SE attacks. Although common systems analyze only landing pages or conduct browser interactions limited to a specific attack, little effort has been made to follow such sequences of web pages to collect multi-step SE attacks.

Sybils are users created for carrying out nefarious actions in online social networks (OSNs) and threaten the security of OSNs. Therefore, Sybil detection is an urgent security task, and various detection methods have been proposed. Existing Sybil detection methods are based on the relationship (i.e., graph structure) of users in OSNs. Structure-based methods can be classified into two categories: Random Walk (RW)-based and Belief Propagation (BP)-based. However, although almost all methods have been experimentally evaluated in terms of their performance and robustness to noise, the theoretical understanding of them is insufficient. In this paper, we interpret the Sybil detection problem from the viewpoint of graph signal processing and provide a framework to formulate RW- and BPbased methods as low-pass filtering. This framework enables us to theoretically compare RW- and BP-based methods and explain why BP-based methods perform well for scale-free graphs, unlike RW-based methods. Furthermore, by this framework, we relate RW- and BP-based methods and Graph Neural Networks (GNNs) and discuss the difference among these methods. Finally, we evaluate the validity of this framework through numerical experiments.

Domain parking is a monetization mechanism for displaying online advertisements in unused domain names. Some domain names used in cyber attacks are known to leverage domain parking services after the attack. However, the temporal relationships between domain parking services and malicious domain names have not been studied well. In this study, we investigated how malicious domain names using domain parking services change over time. We conducted a large-scale measurement study of more than 66.8 million domain names that have used domain parking services in the past 19 months. We reveal the existence of 3,964 domain names that have been malicious after using domain parking. We also reveal the existence of 3.02 million domain names that utilized multiple parking services simultaneously or while switching between them. Our study can contribute to the efficient analysis of malicious domain names using domain parking services.

Account security to protect user accounts against sensitive data breaches is a major mission for online service providers. Therefore, they exert tremendous effort in securing account authentication. Although threats from complete outsiders, such as account hijacking for monetization, still occur, recent studies have shed light on threats to privacy from insiders. This paper sheds light on the latter threats. Specifically, we present the first comprehensive study of an attack from insiders that identifies the existence of a target's account by using the target's email address and insecure login-related messages displayed. Such a threat may violate intimates' or acquaintances' privacy because the kinds of service accounts a user has implies his/her personal preferences or situation. We conducted surveys regarding user expectations and behaviors on online services and a measurement study of the login-related messages on online services that are considered sensitive. We found that over 80% of participants answered that there are sensitive services and that almost all services were vulnerable to our attack. Moreover, about half the participants who have sensitive services are insecurely registered on them and thus could be potential victims. Finally, we make recommendations on the basis of our findings for online service providers to improve login-related messages and for users to take appropriate defensive actions.

With our ever increasing dependence on computers, many governments have started to investigate regulations on vulnerabilities and their lifecycle management. Although many previous works have studied this problem space for mainstream software packages and web applications, few studies have targeted consumer IoT devices. As a first step towards filling this void, this paper presents a pilot study on the vulnerability disclosures and patch release behaviors related to 3 prominent consumer IoT vendors in Japan and 3 in the United States. Our goals include (i) characterizing trends and risks using accurate data that spans a long period, and (ii) identifying problems, challenges, and potential approaches for future studies of this problem space. To this end, we collected all published vulnerabilities and their patches for the consumer IoT products by the included vendors between 2006 and 2017; then, we analyzed our data from multiple perspectives such as the severity of the vulnerabilities and the timing of patch releases with respect to disclosures and exploits. Our work has uncovered several findings that may inform future studies, including (i) a stark contrast in the vulnerability disclosures between the two countries and (ii) three alarming vendor practices that may pose significant risks of 1-day exploits.

In this study, we conduct the first study on the analysis of voice assistant (VA) apps. We first collect the metadata of VA apps from the VA app directory and analyze them. Next, we call VA apps by the corresponding voice commands and examine how they identify users by analyzing the responses from the apps. We found that roughly half of the VA apps performed user identification by some means. We also found that several apps aim to acquire personal information such as birth date, age, or the blood type through voice conversations. As such data will be stored in the cloud, we need to have a mechanism to ensure that an end-user can check/control the data in a usable way.

This paper reports a longitudinal measurement study aiming to understand how mobile app developers are responsive to updates of software libraries over time. To quantify their responsiveness to library updates, we collected 21,046 Android apps, which equated 142,611 unique application package kit (APK) files, each corresponding to a different version of an app. The release dates of these APK files spanned across 9 years. The key findings we derived from our analysis are as follows. (1) We observed an undesirable level of responsiveness of app developers; 50% of library update adoptions by app developers were performed for more than 3 months after the release date of the library, and 50% of outdated libraries used in apps were retained for over 10 months. (2) Deploying a security fix campaign in the app distribution market effectively reduced the number of apps with unfixed vulnerabilities; however, CVE-numbered vulnerabilities (without a campaign) were prone to remain unfixed. (3) The responsiveness of app developers varied and depended on multiple factors, for example, popular apps with a high number of installations had a better response to library updates and, while it took 77 days on average for app developers to adopt version updates for advertising libraries, it took 237 days for updates of utility libraries to be adopted. We discuss practical ways to eliminate libraries with vulnerabilities and to improve the responsiveness of app developers to library updates.

This paper presents a method to reduce the labeling cost when acquiring training data for a system that detects malicious domain names by supervised machine learning. The conventional system requires large quantities of both benign and malicious domain names to be prepared as training data to obtain a classifier with high classification accuracy. In general, malicious domain names are observed less frequently than benign domain names. Therefore, it is difficult to acquire a large number of malicious domain names without a dedicated labeling method. We propose a method based on active learning that labels data around the decision boundary of classification, i.e., in the gray area, and we show that the classification accuracy can be improved by only using approximately 2.5% of the training data used by the conventional system. An additional disadvantage of the conventional system is that, if the classifier is trained with a small amount of training data, its generalization ability cannot be guaranteed. We propose a method based on ensemble learning that integrates multiple classifiers, and we show that the classification accuracy can be stabilized and improved.

As the online advertising market has grown, advertising frauds (ad frauds) have become a serious problem. Countermeasures against ad frauds are evaded since they rely on noticeable features (e.g., burstiness of ad requests) that attackers can easily change. We propose an ad-fraud-detection method that leverages robust features against attacker evasion. We designed novel features on the basis of the statistics observed in an ad network calculated from a large amount of ad requests from legitimate users, such as the popularity of publisher websites and the tendencies of client environments. We assume that attackers cannot know of or manipulate these statistics and that features extracted from fraudulent ad requests tend to be outliers. These features are used to construct a machine-learning model for detecting fraudulent ad requests. We evaluated our proposed method by using ad-request logs observed within an actual ad network. The results revealed that our designed features improved the recall rate by 10% and had about 100,000 - 160,000 fewer false negatives per day than conventional features based on the burstiness of ad requests. In addition, by evaluating detection performance with long-term dataset, we confirmed that the proposed method is robust against performance degradation over time. Finally, we applied our proposed method to a large dataset constructed on an ad network and found several characteristics of the latest ad frauds in the wild, for example, a large amount of fraudulent ad requests is sent from cloud servers.

This study aims to understand the threats caused by abandoned Internet resources used by Android apps. By abandoned, we mean Internet resources that support apps that were published and are still available on the mobile app marketplace, but have not been maintained and hence are at risk for abuse by an outsider. Internet resources include domain names and hard-coded IP addresses, which could be used for nefarious purposes, e.g., stealing sensitive private information, scamming and phishing, click fraud, and injecting malware distribution URL. As a result of the analysis of 1.1 M Android apps published in the official marketplace, we uncovered 3,628 of abandoned Internet resources associated with 7,331 available mobile apps. These resources are subject to hijack by outsiders. Of these apps, 13 apps have been installed more than a million of times, a measure of the breadth of the threat. Based on the findings of empirical experiments, we discuss potential threats caused by abandoned Internet resources and propose countermeasures against these threats.

This paper presents a practical side-channel attack that identifies the social web service account of a visitor to an attacker's website. Our attack leverages the widely adopted user-blocking mechanism, abusing its inherent property that certain pages return different web content depending on whether a user is blocked from another user. Our key insight is that an account prepared by an attacker can hold an attackercontrollable binary state of blocking/non-blocking with respect to an arbitrary user on the same service; provided that the user is logged in to the service, this state can be retrieved as one-bit data through the conventional cross-site timing attack when a user visits the attacker's website. We generalize and refer to such a property as visibility control, which we consider as the fundamental assumption of our attack. Building on this primitive, we show that an attacker with a set of controlled accounts can gain a complete and flexible control over the data leaked through the side channel. Using this mechanism, we show that it is possible to design and implement a robust, largescale user identification attack on a wide variety of social web services. To verify the feasibility of our attack, we perform an extensive empirical study using 16 popular social web services and demonstrate that at least 12 of these are vulnerable to our attack. Vulnerable services include not only popular social networking sites such as Twitter and Facebook, but also other types of web services that provide social features, e.g., eBay and Xbox Live. We also demonstrate that the attack can achieve nearly 100% accuracy and can finish within a sufficiently short time in a practical setting. We discuss the fundamental principles, practical aspects, and limitations of the attack as well as possible defenses.

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.

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.

To expose more users to threats of drive-by download attacks, attackers compromise vulnerable websites discovered by search engines and redirect clients to malicious websites created with exploit kits. Security researchers and vendors have tried to prevent the attacks by detecting malicious data, i.e., malicious URLs, web content, and redirections. However, attackers conceal a part of malicious data with evasion techniques to circumvent detection systems. In this paper, we propose a system for detecting malicious websites without collecting all malicious data. Even if we cannot observe a part of malicious data, we can always observe compromised websites. Since vulnerable websites are discovered by search engines, compromised websites have similar traits. Therefore, we built a classifier by leveraging not only malicious websites but also compromised websites. More precisely, we convert all websites observed at the time of access into a redirection graph and classify it by integrating similarities between its subgraphs and redirection subgraphs shared across malicious, benign, and compromised websites. As a result of evaluating our system with crawling data of 455,860 websites, we found that the system achieved a 91.7% true positive rate for malicious websites containing exploit URLs at a low false positive rate of 0.1%. Moreover, it detected 143 more evasive malicious websites than conventional systems.

Attackers have increased the number of infected hosts by redirecting users of compromised popular websites toward websites that exploit vulnerabilities of a browser and its plugins. To prevent damage, detecting infected hosts based on proxy logs, which are generally recorded on enterprise networks, is gaining attention rather than blacklist-based filtering because creating blacklists has become difficult due to the short lifetime of malicious domains and concealment of exploit code. Since information extracted from one URL is limited, we focus on a sequence of URLs that includes artifacts of malicious redirections. We propose a system for detecting malicious URL sequences from proxy logs with a low false positive rate. To elucidate an effective approach of malicious URL sequence detection, we compared three approaches: individual-based approach, convolutional neural network (CNN), and our newly developed event de-noising CNN (EDCNN). Our EDCNN is a new CNN to reduce the negative effect of benign URLs redirected from compromised websites included in malicious URL sequences. Our evaluation shows that the EDCNN lowers the operation cost of malware infection by reducing 47% of false alerts compared with a CNN when users access compromised websites but do not obtain exploit code due to browser fingerprinting.

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.

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.

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.

Malware authors or attackers always try to evade detection methods to accomplish their mission. Such detection methods are broadly divided into three types: static feature, host-behavior, and network-behavior based. Static feature-based methods are evaded using packing techniques. Host- behavior-based methods also can be evaded using some code injection methods, such as API hook and dynamic link library hook. This arms race regarding static feature-based and host-behavior- based methods increases the importance of network-behavior-based methods. The necessity of communication between infected hosts and attackers makes it difficult to evade network-behavior- based methods. The effectiveness of such methods depends on how we collect a variety of communications by using malware samples. However, analyzing all new malware samples for a long period is infeasible. Therefore, we propose a method for determining whether dynamic analysis should be suspended based on network behavior to collect malware communications efficiently and exhaustively. The key idea behind our proposed method is focused on two characteristics of malware communication: the change in the communication purpose and the common latent function. These characteristics of malware communications resemble those of natural language from the viewpoint of data structure, and sophisticated analysis methods have been proposed in the field of natural language processing. For this reason, we applied the recursive neural network, which has recently exhibited high classification performance, to our proposed method. In the evaluation with 29,562 malware samples, our proposed method reduced 67.1% of analysis time while keeping the coverage of collected URLs to 97.9% of the method that continues full analyses.

Detecting malicious web pages used in attacks and building blacklists and signatures from them are done to protect users against drive-by download attacks. Gathering the content on web pages by crawling and evaluating it to check if it is malicious can help in detecting malicious web pages. Methods that apply supervised machine learning to this evaluation are proposed for detecting malicious web pages from a massive amount of web pages. However, these methods need manual inspections for preparing training data when classifiers are retrained in accordance with changes in the content on malicious web pages. In this paper, we propose a method that evaluates whether web pages are malicious and needs only the discrimination results of web pages identified by high-interaction honeyclients to prepare training data. This method evaluates maliciousness on the basis of the structural similarity of redirection chains arising from drive-by download attacks. The results of our experiments with two years of data showed that the accuracy of our method was about 20\% higher than that of the previous method.

Drive-by download attacks force users to automatically download and install malware by redirecting them to malicious URLs that exploit vulnerabilities of the user's web browser. Attackers profile the information on the user's environment such as the name and version of the browser and browser plugins and launch a drive-by download attack on only certain targets by changing the destination URL. When malicious content detection and collection techniques such as honey clients are used that do not match the specific environment of the attack target, they cannot detect the attack because they are not redirected. We propose here a method to exhaustively analyze Java Script code relevant to redirections and to extract the destination URLs in the code. Our method facilitates the detection of attacks by extracting a large number of URLs while controlling the analysis overhead by excluding code not relevant to redirections. We implemented our method in a browser emulator called Mine Spider that automatically extracts potential URLs from websites. We validated it by using communication data with malicious websites captured during a three-year period. The experimental results demonstrated that Mine Spider extracted 30,000 new URLs from websites in a few seconds that existing techniques missed.

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. Auto-BLG 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.