Social networks offer convenient ways to seamlessly reach out to large audiences. In particular, Facebook pages are increasingly used by businesses, brands, and organizations to connect with multitudes of users worldwide. As the number of likes of a page has become a de-facto measure of its popularity and profitability, an underground market of services artificially inflating page likes, aka like farms, has emerged alongside Facebook's official targeted advertising platform. Nonetheless, there is little work that systematically analyzes Facebook pages' promotion methods. Aiming to fill this gap, we present a honeypot-based comparative measurement study of page likes garnered via Facebook advertising and from popular like farms. First, we analyze likes based on demographic, temporal, and social characteristics, and find that some farms seem to be operated by bots and do not really try to hide the nature of their operations, while others follow a stealthier approach, mimicking regular users' behavior. Next, we look at fraud detection algorithms currently deployed by Facebook and show that they do not work well to detect stealthy farms which spread likes over longer timespans and like popular pages to mimic regular users. To overcome their limitations, we investigate the feasibility of timeline-based detection of like farm accounts, focusing on characterizing content generated by Facebook accounts on their timelines as an indicator of genuine versus fake social activity. We analyze a range of features, grouped into two main categories: lexical and non-lexical. We find that like farm accounts tend to re-share content, use fewer words and poorer vocabulary, and more often generate duplicate comments and likes compared to normal users. Using relevant lexical and non-lexical features, we build a classifier to detect like farms accounts that achieves precision higher than 99 and 93 recall.
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@cite_52: Peer-to-peer and other decentralized,distributed systems are known to be particularly vulnerable to sybil attacks. In a sybil attack,a malicious user obtains multiple fake identities and pretends to be multiple, distinct nodes in the system. By controlling a large fraction of the nodes in the system,the malicious user is able to "out vote" the honest users in collaborative tasks such as Byzantine failure defenses. This paper presents SybilGuard, a novel protocol for limiting the corruptive influences of sybil attacks.Our protocol is based on the "social network "among user identities, where an edge between two identities indicates a human-established trust relationship. Malicious users can create many identities but few trust relationships. Thus, there is a disproportionately-small "cut" in the graph between the sybil nodes and the honest nodes. SybilGuard exploits this property to bound the number of identities a malicious user can create.We show the effectiveness of SybilGuard both analytically and experimentally.
@cite_32: Many online games have their own ecosystems, where players can purchase in-game assets using game money. Players can obtain game money through active participation or “real money trading” through official channels: converting real money into game money. The unofficial market for real money trading gave rise to gold farming groups (GFGs), a phenomenon with serious impact in the cyber and real worlds. GFGs in massively multiplayer online role-playing games (MMORPGs) are some of the most interesting underground cyber economies because of the massive nature of the game. To detect GFGs, there have been various studies using behavioral traits. However, they can only detect gold farmers, not entire GFGs with internal hierarchies. Even worse, GFGs continuously develop techniques to hide, such as forming front organizations, concealing cyber-money, and changing trade patterns when online game service providers ban GFGs. In this paper, we analyze the characteristics of the ecosystem of a large-scale MMORPG, and devise a method for detecting GFGs. We build a graph that characterizes virtual economy transactions, and trace abnormal trades and activities. We derive features from the trading graph and physical networks used by GFGs to identify them in their entirety. Using their structure, we provide recommendations to defend effectively against GFGs while not affecting the existing virtual ecosystem.
@cite_40: SybilInfer is an algorithm for labelling nodes in a social network as honest users or Sybils controlled by an adversary. At the heart of SybilInfer lies a probabilistic model of honest social networks, and an inference engine that returns potential regions of dishonest nodes. The Bayesian inference approach to Sybil detection comes with the advantage label has an assigned probability, indicating its degree of certainty. We prove through analytical results as well as experiments on simulated and real-world network topologies that, given standard constraints on the adversary, SybilInfer is secure, in that it successfully distinguishes between honest and dishonest nodes and is not susceptible to manipulation by the adversary. Furthermore, our results show that SybilInfer outperforms state of the art algorithms, both in being more widely applicable, as well as providing vastly more accurate results.
@cite_45: Users increasingly rely on the trustworthiness of the information exposed on Online Social Networks (OSNs). In addition, OSN providers base their business models on the marketability of this information. However, OSNs suffer from abuse in the form of the creation of fake accounts, which do not correspond to real humans. Fakes can introduce spam, manipulate online rating, or exploit knowledge extracted from the network. OSN operators currently expend significant resources to detect, manually verify, and shut down fake accounts. Tuenti, the largest OSN in Spain, dedicates 14 full-time employees in that task alone, incurring a significant monetary cost. Such a task has yet to be successfully automated because of the difficulty in reliably capturing the diverse behavior of fake and real OSN profiles. We introduce a new tool in the hands of OSN operators, which we call SybilRank. It relies on social graph properties to rank users according to their perceived likelihood of being fake (Sybils). SybilRank is computationally efficient and can scale to graphs with hundreds of millions of nodes, as demonstrated by our Hadoop prototype. We deployed SybilRank in Tuenti's operation center. We found that ∼90 of the 200K accounts that SybilRank designated as most likely to be fake, actually warranted suspension. On the other hand, with Tuenti's current user-report-based approach only ∼5 of the inspected accounts are indeed fake.
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@cite_46: Detecting fake accounts in online social networks (OSNs) protects OSN operators and their users from various malicious activities. Most detection mechanisms attempt to predict and classify user accounts as real (i.e., benign, honest) or fake (i.e., malicious, Sybil) by analyzing user-level activities or graph-level structures. These mechanisms, however, are not robust against adversarial attacks in which fake accounts cloak their operation with patterns resembling real user behavior. We herein demonstrate that victims, benign users who control real accounts and have befriended fakes, form a distinct classification category that is useful for designing robust detection mechanisms. First, as attackers have no control over victim accounts and cannot alter their activities, a victim account classifier which relies on user-level activities is relatively harder to circumvent. Second, as fakes are directly connected to victims, a fake account detection mechanism that integrates victim prediction into graphlevel structures is more robust against manipulations of the graph. To validate this new approach, we designed Integro, a scalable defense system that helps OSNs detect fake accounts using a meaningful a user ranking scheme. Integro starts by predicting victim accounts from user-level activities. After that, it integrates these predictions into the graph as weights, so that edges incident to predicted victims have much lower weights than others. Finally, Integro ranks user accounts based on a modified random walk that starts from a known real account. Integro guarantees that most real accounts rank higher than fakes so that OSN operators can take actions against low-ranking fake accounts. We implemented Integro using widely-used, open-source distributed computing platforms in which it scaled nearly linearly. We evaluated Integro against SybilRank, the state-of-the-art in fake account detection, using real-world datasets and a largescale deployment at Tuenti, the largest OSN in Spain. We show that Integro significantly outperforms SybilRank in user ranking quality, where the only requirement is to employ a victim classifier is better than random. Moreover, the deployment of Integro at Tuenti resulted in up to an order of magnitude higher precision in fake accounts detection, as compared to SybilRank.
Prior work has focused quite extensively on the analysis and the detection of sybil and or fake accounts in online social networks by relying on tightly-knit community structures @cite_52 @cite_40 @cite_2 @cite_45 @cite_37 @cite_46 . By contrast, we work to detect accounts that are employed by like farms to boost the number of Facebook page likes, whether they are operated by a bot or a human. We highlight several characteristics about the social structure and activity of fake profiles attracted by the honeypot pages, e.g., their interconnected nature or the activity bursts. In fact, our analysis does not only confirm a few insights used by sybil detection algorithms but also reveals new patterns that could complement them. Fraud and fake activities are not restricted to social network, but widespread also on other platforms, such as online gaming. In this context, @cite_5 rely on self-similarity to effectively measure the frequency of repeated activities per player over time, and use it to identify bots. Also, @cite_32 analyze the characteristics of the ecosystem of multiplayer online role-playing games (MMORPGs), and devise a method for detecting gold farming groups (GFGs), based on graph techniques.