Early detection of spam-related activity

Spam, the distribution of unsolicited bulk email, is a big security threat on the Internet. Recent studies show approximately 70-90% of the worldwide email traffic—about 70 billion messages a day—is spam. Spam consumes resources on the network and at mail servers, and it is also used to launch other attacks on users, such as distributing malware or phishing. Spammers have increased their virulence and resilience by sending spam from large collections of compromised machines (“botnets”). Spammers also make heavy use of URLs and domains to direct victims to point-of-sale Web sites, and miscreants register large number of domains to evade blacklisting efforts. To mitigate the threat of spam, users and network administrators need proactive techniques to distinguish spammers from legitimate senders and to take down online spam-advertised sites. In this dissertation, we focus on characterizing spam-related activities and developing systems to detect them early. Our work builds on the observation that spammers need to acquire attack agility to be profitable, which presents differences in how spammers and legitimate users interact with Internet services and exposes detectable during early period of attack. We examine several important components across the spam life cycle, including spam dissemination that aims to reach users' inboxes, the hosting process during which spammers set DNS servers and Web servers, and the naming process to acquire domain names via registration services. We first develop a new spam-detection system based on network-level features of spamming bots. These lightweight features allow the system to scale better and to be more robust. Next, we analyze DNS resource records and lookups from top-level domain servers during the initial stage after domain registrations, which provides a global view across the Internet to characterize spam hosting infrastructure. We further examine the domain registration process and present the unique registration behavior of spammers. Finally, we build an early-warning system to identify spammer domains at time-of-registration rather than later at time-of-use. We have demonstrated that our detection systems are effective by using real-world datasets. Our work has also had practical impact. Some of the network-level features that we identified have since been incorporated into spam filtering products at Yahoo! and McAfee, and our work on detecting spammer domains at time-of-registration has directly influenced new projects at Verisign to investigate domain registrations.Ph.D

Herding Vulnerable Cats: A Statistical Approach to Disentangle Joint Responsibility for Web Security in Shared Hosting

Hosting providers play a key role in fighting web compromise, but their ability to prevent abuse is constrained by the security practices of their own customers. {\em Shared} hosting, offers a unique perspective since customers operate under restricted privileges and providers retain more control over configurations. We present the first empirical analysis of the distribution of web security features and software patching practices in shared hosting providers, the influence of providers on these security practices, and their impact on web compromise rates. We construct provider-level features on the global market for shared hosting -- containing 1,259 providers -- by gathering indicators from 442,684 domains. Exploratory factor analysis of 15 indicators identifies four main latent factors that capture security efforts: content security, webmaster security, web infrastructure security and web application security. We confirm, via a fixed-effect regression model, that providers exert significant influence over the latter two factors, which are both related to the software stack in their hosting environment. Finally, by means of GLM regression analysis of these factors on phishing and malware abuse, we show that the four security and software patching factors explain between 10\% and 19\% of the variance in abuse at providers, after controlling for size. For web-application security for instance, we found that when a provider moves from the bottom 10\% to the best-performing 10\%, it would experience 4 times fewer phishing incidents. We show that providers have influence over patch levels--even higher in the stack, where CMSes can run as client-side software--and that this influence is tied to a substantial reduction in abuse levels

Security for Decentralised Service Location - Exemplified with Real-Time Communication Session Establishment

Decentralised Service Location, i.e. finding an application communication endpoint based on a Distributed Hash Table (DHT), is a fairly new concept. The precise security implications of this approach have not been studied in detail. More importantly, a detailed analysis regarding the applicability of existing security solutions to this concept has not been conducted. In many cases existing client-server approaches to security may not be feasible. In addition, to understand the necessity for such an analysis, it is key to acknowledge that Decentralised Service Location has some unique security requirements compared to other P2P applications such as filesharing or live streaming. This thesis concerns the security challenges for Decentralised Service Location. The goals of our work are on the one hand to precisely understand the security requirements and research challenges for Decentralised Service Location, and on the other hand to develop and evaluate corresponding security mechanisms. The thesis is organised as follows. First, fundamentals are explained and the scope of the thesis is defined. Decentralised Service Location is defined and P2PSIP is explained technically as a prototypical example. Then, a security analysis for P2PSIP is presented. Based on this security analysis, security requirements for Decentralised Service Location and the corresponding research challenges -- i.e. security concerns not suitably mitigated by existing solutions -- are derived. Second, several decentralised solutions are presented and evaluated to tackle the security challenges for Decentralised Service Location. We present decentralised algorithms to enable availability of the DHTs lookup service in the presence of adversary nodes. These algorithms are evaluated via simulation and compared to analytical bounds. Further, a cryptographic approach based on self-certifying identities is illustrated and discussed. This approach enables decentralised integrity protection of location-bindings. Finally, a decentralised approach to assess unknown identities is introduced. The approach is based on a Web-of-Trust model. It is evaluated via prototypical implementation. Finally, the thesis closes with a summary of the main contributions and a discussion of open issues

Measuring for privacy: From tracking to cloaking

We rely on various types of online services to access information for different uses, and often provide sensitive information during the interactions with these services. These online services are of different types; e.g. commercial websites (e.g., banking, education, news, shopping, dating, social media), essential websites (e.g., government). Online services are available through websites as well as mobile apps. The growth of web sites, mobile devices and apps that run on those devices, have resulted in the proliferation of online services. This whole ecosystem of online services had created an environment where everyone using it are being tracked. Several past studies have performed privacy measurements to assess the prevalence of tracking in online services. Most of these studies used institutional (i.e., non-residential) resources for their measurements, and lacked global perspective. Tracking on online services and its impact to privacy may differ at various locations. Therefore, to fill in this gap, we perform a privacy measurement study of popular commercial websites, using residential networks from various locations. Unlike commercial online services, there are different categories (e.g., government, hospital, religion) of essential online services where users do not expect to be tracked. The users of these essential online services often use information of extreme personal and sensitive in nature (e.g., social insurance number, health information, prayer requests/confessions made to a religious minister) when interacting with those services. However, contrary to the expectations of users, these essential services include user tracking capabilities. We built frameworks to perform privacy measurements of these online services (include both web sites and Android apps) that are of different types (i.e., governments, hospitals and religious services in jurisdictions around the world). The instrumented tracking metrics (i.e., stateless, stateful, session replaying) from the privacy measurements of these online services are then analyzed. Malicious sites (e.g., phishing) mimic online services to deceive users, causing them harm. We found 80% of analyzed malicious sites are cloaked, and not blocked by search engine crawlers. Therefore, sensitive information collected from users through these sites is exposed. In addition, underlying Internet-connected infrastructure (e.g., networked devices such as routers, modems) used by online users, can suffer from security issues due to nonuse of TLS or use of weak SSL/TLS certificates. Such security issues (e.g., spying on a CCTV camera) can compromise data integrity, confidentiality and user privacy. Overall, we found tracking on commercial websites differ based on the location of corresponding residential users. We also observed widespread use of tracking by commercial trackers, and session replay services that expose sensitive information from essential online services. Sensitive information are also exposed due to vulnerabilities in online services (e.g., Cross Site Scripting). Furthermore, a significant proportion of malicious sites evade detection by security/search engine crawlers, which may make such sites readily available to users. We also detect weaknesses in the TLS ecosystem of Internet-connected infrastructure that supports running these online services. These observations require more research on privacy of online services, as well as information exposure from malicious online services, to understand the significance of privacy issues, and to adopt appropriate mitigation strategies

An integrated networkbased mobile botnet detection system

The increase in the use of mobile devices has made them target for attackers, through the use of sophisticated malware. One of the most significant types of such malware is mobile botnets. Due to their continually evolving nature, botnets are difficult to tackle through signature and traditional anomaly based detection methods. Machine learning techniques have also been used for this purpose. However, the study of their effectiveness has shown methodological weaknesses that have prevented the emergence of conclusive and thorough evidence about their merit. To address this problem, in this thesis we propose a mobile botnet detection system, called MBotCS and report the outcomes of a comprehensive experimental study of mobile botnet detection using supervised machine learning techniques to analyse network traffic and system calls on Android mobile devices. The research covers a range of botnet detection scenarios that is wider from what explored so far, explores atomic and box learning algorithms, and investigates thoroughly the sensitivity of the algorithm performance on different factors (algorithms, features of network traffic, system call data aggregation periods, and botnets vs normal applications and so on). These experiments have been evaluated using real mobile device traffic, and system call captured from Android mobile devices, running normal apps and mobile botnets. The experiments study has several superiorities comparing with existing research. Firstly, experiments use not only atomic but also box ML classifiers. Secondly, a comprehensive set of Android mobile botnets, which had not been considered previously, without relying on any form of synthetic training data. Thirdly, experiments contain a wider set of detection scenarios including unknown botnets and normal applications. Finally, experiments include the statistical significance of differences in detection performance measures with respect to different factors. The study resulted in positive evidence about the effectiveness of the supervised learning approach, as a solution to the mobile botnet detection problem