West, Andrew G.
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Publication CleanURL: A Privacy Aware Link Shortener(2012-01-01) Kim, Daniel; Su, Kevin; West, Andrew G.; Aviv, AdamWhen URLs containing application parameters are posted in public settings privacy can be compromised if the those arguments contain personal or tracking data. To this end we describe a privacy aware link shortening service that attempt to strip sensitive and non-essential parameters based on difference algorithms and human feedback. Our implementation, CleanURL, allows users to validate our automated logic and provides them with intuition about how these otherwise opaque arguments function. Finally, we apply CleanURL over a large Twitter URL corpus to measure the prevalence of such privacy leaks and further motivate our tool.Publication Wikipedia Vandalism Detection: Combining Natural Language, Metadata, and Reputation Features(2011-02-01) Adler, B. Thomas; de Alfaro, Luca; Mola-Velasco, Santiago M; Rosso, Paolo; West, Andrew G.Wikipedia is an online encyclopedia which anyone can edit. While most edits are constructive, about 7% are acts of vandalism. Such behavior is characterized by modifications made in bad faith; introducing spam and other inappropriate content. In this work, we present the results of an effort to integrate three of the leading approaches to Wikipedia vandalism detection: a spatio-temporal analysis of metadata (STiki), a reputation-based system (WikiTrust), and natural language processing features. The performance of the resulting joint system improves the state-of-the-art from all previous methods and establishes a new baseline for Wikipedia vandalism detection. We examine in detail the contribution of the three approaches, both for the task of discovering fresh vandalism, and for the task of locating vandalism in the complete set of Wikipedia revisions.Publication Autonomous Link Spam Detection in Purely Collaborative Environments(2011-10-05) West, Andrew G.; Agrawal, Avantika; Baker, Phillip; Exline, Brittney; Lee, InsupCollaborative models (e.g., wikis) are an increasingly prevalent Web technology. However, the open-access that defines such systems can also be utilized for nefarious purposes. In particular, this paper examines the use of collaborative functionality to add inappropriate hyperlinks to destinations outside the host environment (i.e., link spam). The collaborative encyclopedia, Wikipedia, is the basis for our analysis. Recent research has exposed vulnerabilities in Wikipedia's link spam mitigation, finding that human editors are latent and dwindling in quantity. To this end, we propose and develop an autonomous classifier for link additions. Such a system presents unique challenges. For example, low barriers-to-entry invite a diversity of spam types, not just those with economic motivations. Moreover, issues can arise with how a link is presented (regardless of the destination). In this work, a spam corpus is extracted from over 235,000 link additions to English Wikipedia. From this, 40+ features are codified and analyzed. These indicators are computed using "wiki" metadata, landing site analysis, and external data sources. The resulting classifier attains 64% recall at 0.5% false-positives (ROC-AUC=0.97). Such performance could enable egregious link additions to be blocked automatically with low false-positive rates, while prioritizing the remainder for human inspection. Finally, a live Wikipedia implementation of the technique has been developed.Publication Spamming for Science: Active Measurement in Web 2.0 Abuse Research(2012-03-02) West, Andrew G.; Hayati, Pedram; Potdar, Vidyasagar; Lee, InsupSpam and other electronic abuses have long been a focus of computer security research. However, recent work in the domain has emphasized an economic analysis of these operations in the hope of understanding and disrupting the profit model of attackers. Such studies do not lend themselves to passive measurement techniques. Instead, researchers have become middle-men or active participants in spam behaviors; methodologies that lie at an interesting juncture of legal, ethical, and human subject e.g., IRB) guidelines. In this work two such experiments serve as case studies: One testing a novel link spam model on Wikipedia and another using blackhat software to target blog comments and forums. Discussion concentrates on the experimental design process, especially as in uenced by human-subject policy. Case studies are used to frame related work in the area, and scrutiny reveals the computer science community requires greater consistency in evaluating research of this nature.Publication Towards Content-Driven Reputation for Collaborative Code Repositories(2012-08-28) West, Andrew G.; Lee, InsupAs evidenced by SourceForge and GitHub, code repositories now integrate Web 2.0 functionality that enables global participation with minimal barriers-to-entry. To prevent detrimental contributions enabled by crowdsourcing, reputation is one proposed solution. Fortunately this is an issue that has been addressed in analogous version control systems such as the *wiki* for natural language content. The WikiTrust algorithm ("content-driven reputation"), while developed and evaluated in wiki environments operates under a possibly shared collaborative assumption: actions that "survive" subsequent edits are reflective of good authorship. In this paper we examine WikiTrust's ability to measure author quality in collaborative code development. We first define a mapping from repositories to wiki environments and use it to evaluate a production SVN repository with 92,000 updates. Analysis is particularly attentive to reputation loss events and attempts to establish ground truth using commit comments and bug tracking. A proof-of-concept evaluation suggests the technique is promising (about two-thirds of reputation loss is justified) with false positives identifying areas for future refinement. Equally as important, these false positives exemplify differences in content evolution and the cooperative process between wikis and code repositories.Publication Trust in Collaborative Web Applications(2012-01-01) West, Andrew G.; Chang, Jian; Venkatasubramanian, Krishna; Lee, InsupCollaborative functionality is increasingly prevalent in web applications. Such functionality permits individuals to add - and sometimes modify - web content, often with minimal barriers to entry. Ideally, large bodies of knowledge can be amassed and shared in this manner. However, such software also provide a medium for nefarious persons to operate. By determining the extent to which participating content/agents can be trusted, one can identify useful contributions. In this work, we define the notion of trust for Collaborative Web Applications and survey the state-of-the-art for calculating, interpreting, and presenting trust values. Though techniques can be applied broadly, Wikipedia's archetypal nature makes it a focal point for discussion.Publication Link Spamming Wikipedia for Profit(2011-09-01) West, Andrew G.; Chang, Jian; Venkatasubramanian, Krishna; Sokolsky, Oleg; Lee, InsupCollaborative functionality is an increasingly prevalent web technology. To encourage participation, these systems usually have low barriers-to-entry and permissive privileges. Unsurprisingly, ill-intentioned users try to leverage these characteristics for nefarious purposes. In this work, a particular abuse is examined -- link spamming -- the addition of promotional or otherwise inappropriate hyperlinks. Our analysis focuses on the "wiki" model and the collaborative encyclopedia, Wikipedia, in particular. A principal goal of spammers is to maximize *exposure*, the quantity of people who view a link. Creating and analyzing the first Wikipedia link spam corpus, we find that existing spam strategies perform quite poorly in this regard. The status quo spamming model relies on link persistence to accumulate exposures, a strategy that fails given the diligence of the Wikipedia community. Instead, we propose a model that exploits the latency inherent in human anti-spam enforcement. Statistical estimation suggests our novel model would produce significantly more link exposures than status quo techniques. More critically, the strategy could prove economically viable for perpetrators, incentivizing its exploitation. To this end, we address mitigation strategies.Publication Multilingual Vandalism Detection Using Language-Independent & Ex Post Facto Evidence(2011-09-21) West, Andrew G.; Lee, InsupThere is much literature on Wikipedia vandalism detection. However, this writing addresses two facets given little treatment to date. First, prior efforts emphasize zero-delay detection, classifying edits the moment they are made. If classification can be delayed (e.g., compiling offline distributions), it is possible to leverage ex post facto evidence. This work describes/evaluates several features of this type, which we find to be overwhelmingly strong vandalism indicators. Second, English Wikipedia has been the primary test-bed for research. Yet, Wikipedia has 200+ language editions and use of localized features impairs portability. This work implements an extensive set of language-independent indicators and evaluates them using three corpora (German, English, Spanish). The work then extends to include language-specific signals. Quantifying their performance benefit, we find that such features can moderately increase classifier accuracy, but significant effort and language fluency are required to capture this utility. Aside from these novel aspects, this effort also broadly addresses the task, implementing 65 total features. Evaluation produces 0.840 PR-AUC on the zero-delay task and 0.906 PR-AUC with ex post facto evidence (averaging languages). Performance matches the state-of-the-art (English), sets novel baselines (German, Spanish), and is validated by a first-place finish over the 2011 PAN-CLEF test set.Publication An Evaluation Framework for Reputation Management Systems(2009-05-10) West, Andrew G; Kannan, Sampath; Lee, Insup; Sokolsky, OlegReputation management (RM) is employed in distributed and peer-to-peer networks to help users compute a measure of trust in other users based on initial belief, observed behavior, and run-time feedback. These trust values influence how, or with whom, a user will interact. Existing literature on RM focuses primarily on algorithm development, not comparative analysis. To remedy this, we propose an evaluation framework based on the trace-simulator paradigm. Trace file generation emulates a variety of network configurations, and particular attention is given to modeling malicious user behavior. Simulation is trace-based and incremental trust calculation techniques are developed to allow experimentation with networks of substantial size. The described framework is available as open source so that researchers can evaluate the effectiveness of other reputation management techniques and/or extend functionality. This chapter reports on our framework’s design decisions. Our goal being to build a general-purpose simulator, we have the opportunity to characterize the breadth of existing RM systems. Further, we demonstrate our tool using two reputation algorithms (EigenTrust and a modified TNA-SL) under varied network conditions. Our analysis permits us to make claims about the algorithms’ comparative merits. We conclude that such systems, assuming their distribution is secure, are highly effective at managing trust, even against adversarial collectives.Publication Spam Mitigation Using Spatio-Temporal Reputations From Blacklist History(2010-12-01) West, Andrew G.; Aviv, Adam J.; Chang, Jian; Lee, InsupIP blacklists are a spam filtering tool employed by a large number of email providers. Centrally maintained and well regarded, blacklists can filter 80+% of spam without having to perform computationally expensive content-based filtering. However, spammers can vary which hosts send spam (often in intelligent ways), and as a result, some percentage of spamming IPs are not actively listed on any blacklist. Blacklists also provide a previously untapped resource of rich historical information. Leveraging this history in combination with spatial reasoning, this paper presents a novel reputation model (PreSTA), designed to aid in spam classification. In simulation on arriving email at a large university mail system, PreSTA is capable of classifying up to 50% of spam not identified by blacklists alone, and 93% of spam on average (when used in combination with blacklists). Further, the system is consistent in maintaining this blockage-rate even during periods of decreased blacklist performance. PreSTA is scalable and can classify over 500,000 emails an hour. Such a system can be implemented as a complementary blacklist service and used as a first-level filter or prioritization mechanism on an email server.