As popular tools for spreading spam and malware, Sybils (or fake accounts) pose a serious threat to online communities such as Online Social Networks (OSNs). Today, sophisticated attackers are creating realistic Sybils that effectively befriend legitimate users, rendering most automated Sybil detection techniques ineffective. In this paper, we explore the feasibility of a crowdsourced Sybil detection system for OSNs. We conduct a large user study on the ability of humans to detect today's Sybil accounts, using a large corpus of ground-truth Sybil accounts from the Facebook and Renren networks. We analyze detection accuracy by both "experts" and "turkers" under a variety of conditions, and find that while turkers vary significantly in their effectiveness, experts consistently produce near-optimal results. We use these results to drive the design of a multi-tier crowdsourcing Sybil detection system. Using our user study data, we show that this system is scalable, and can be highly effective either as a standalone system or as a complementary technique to current tools.
@cite_0: Consumers increasingly rate, review and research products online. Consequently, websites containing consumer reviews are becoming targets of opinion spam. While recent work has focused primarily on manually identifiable instances of opinion spam, in this work we study deceptive opinion spam---fictitious opinions that have been deliberately written to sound authentic. Integrating work from psychology and computational linguistics, we develop and compare three approaches to detecting deceptive opinion spam, and ultimately develop a classifier that is nearly 90 accurate on our gold-standard opinion spam dataset. Based on feature analysis of our learned models, we additionally make several theoretical contributions, including revealing a relationship between deceptive opinions and imaginative writing.
@cite_31: Today, several actors within the Internet's burgeoning underground economy specialize in providing services to like-minded criminals. At the same time, gray and white markets exist for services on the Internet providing reasonably similar products. In this paper we explore a hypothetical arbitrage between these two markets by purchasing "Human Intelligence" on Amazon's Mechanical Turk service, determining the vulnerability of and cost to compromise the computers being used by the humans to provide this service, and estimating the underground value of the computers which are vulnerable to exploitation. We show that it is economically feasible for an attacker to purchase access to high value hosts via Mechanical Turk, compromise the subset with unpatched, vulnerable browser plugins, and sell access to these hosts via Pay-Per-Install programs for a tidy profit. We also present supplementary statistics gathered regarding Mechanical Turk workers' browser security, antivirus usage, and willingness to run arbitrary programs in exchange for a small monetary reward.
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@cite_17: Modern Web services inevitably engender abuse, as attackers find ways to exploit a service and its user base. However, while defending against such abuse is generally considered a technical endeavor, we argue that there is an increasing role played by human labor markets. Using over seven years of data from the popular crowd-sourcing site Freelancer.com, as well data from our own active job solicitations, we characterize the labor market involved in service abuse. We identify the largest classes of abuse work, including account creation, social networking link generation and search engine optimization support, and characterize how pricing and demand have evolved in supporting this activity.
In this study, we propose using crowdsourcing to solve a challenging OSN security problem. However, many studies have demonstrated how crowdsourcing can be used by attackers for malicious ends. Studies have observed malicious HITs asking turkers to send social spam @cite_12 , perform search engine optimization (SEO) @cite_17 , write fake reviews @cite_0 , and even install malware on their systems @cite_31 .