Exploring the design space of social network-based Sybil defenses

Viswanath, Bimal; Mondal, Mainack; Clement, Allen; Druschel, Peter; Gummadi, Krishna P.; Mislove, Alan; Post, Ansley

doi:10.1109/comsnets.2012.6151333

Cited by 41 publications

(30 citation statements)

References 20 publications

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“…In another study, Yu provided a concise summary of existing Sybil defenses and described their working principles under the classical scenario [15]. Viswanath et al further explored the design space for OSN-based defenses [16], and Boshmaf et al presented a framework for the evaluation of graph-based Sybil detection [17]. Our work is substantially different from such studies: First, we analyze the performance of Sybil defenses in a completely new scenario, i.e., the modern scenario (Sec.…”

Section: Introductionmentioning

confidence: 99%

On the state of OSN-based Sybil defenses

Koll¹,

Stein

et al. 2014

2014 IFIP Networking Conference

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Abstract-A Sybil attack can inject many forged identities (called Sybils) to subvert a target system. Because of the severe damage that Sybil attacks can cause to a wide range of networking applications, there has been a proliferation of Sybil defense schemes. Of particular attention are those that explore the online social networks (OSNs) of users in a victim system in different ways. Unfortunately, while effective Sybil defense solutions are urgently needed, it is unclear how effective these OSN-based solutions are under different contexts. For example, all current approaches have focused on a common, classical scenario where it is difficult for an attacker to link Sybils with honest users and create attack edges; however, researchers have found recently that a modern scenario also becomes typical where an attacker can employ simple strategies to obtain many attack edges.In this work we analyze the state of OSN-based Sybil defenses. Our objective is not to design yet another solution, but rather to thoroughly analyze, measure, and compare how well or inadequate the well-known existing OSN-based approaches perform under both the classical scenario and the modern scenario. Although these approaches mostly perform well under the classical scenario, we find that under the modern scenario they are vulnerable to Sybil attacks. As shown in our quantitative analysis, very often a Sybil only needs a handful of attack edges to disguise itself as a benign node, and there is only a limited success in tolerating Sybils. Our study further points to capabilities a new solution must possess; in particular, in defense against Sybils under the modern scenario, we anticipate a new approach that enriches the structure of a social graph with more information about the relations between its users can work more effectively.

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Section: Introductionmentioning

confidence: 99%

On the state of OSN-based Sybil defenses

Koll¹,

Stein

et al. 2014

2014 IFIP Networking Conference

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“…Finally, there are effective solutions such as [38], [39] to detecting Sybil accounts in distributed systems under the control of a single attacker. These solutions commonly assume that the link between two accounts corresponds to a real social relationship difficult to establish and impossible to forge.…”

Section: ) Impact Of Different Actions On Twittermentioning

confidence: 99%

On the impact of social botnets for spam distribution and digital-influence manipulation

Zhang

et al. 2013

2013 IEEE Conference on Communications and Network Security (CNS)

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Abstract-Online social networks (OSNs) are increasingly threatened by social bots which are software-controlled OSN accounts that mimic human users with malicious intentions. A social botnet refers to a group of social bots under the control of a single botmaster, which collaborate to conduct malicious behavior, while at the same time mimicking the interactions among normal OSN users to reduce their individual risk of being detected. We demonstrate the effectiveness and advantages of exploiting a social botnet for spam distribution and digitalinfluence manipulation through real experiments on Twitter and also trace-driven simulations. Our results can help understand the potentially detrimental effects of social botnets and help OSNs improve their bot(net) detection systems.

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“…The regular adversary α r has a complete control over the Sybil region S, but cannot establish more than O(n/T ( )) attack edges, where T ( ) is the mixing time of the Markov chain over G N . This bound is typically assumed in GSD algorithms [11], [12], as one expects the adversary to require non-trivial social engineering capabilities in order to establish many attack edges. The social adversary α s , however, is more resourceful and has the same capabilities of α r but can establish arbitrarily large number of attack edges, which is motivated by recent empirical results in online social networks [4], [30].…”

Section: B Adversary Modelmentioning

confidence: 99%

“…Given a graph G of a system, state-of-the-art Graph-based Sybil Detection (GSD) algorithms operate on G by making the following assumptions [11], [12]: First, the system owner knows at least one honest identity in G. Second, the adversary cannot establish arbitrarily many relationships between Sybil and honest identities. Third, the subgraph induced by the set of honest identities is fast mixing.…”

Section: B Graph-based Sybil Detectionmentioning

confidence: 99%

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Graph-based Sybil detection in social and information systems

Boshmaf

Beznosov

Ripeanu

2013

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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Abstract-Sybil attacks in social and information systems have serious security implications. Out of many defence schemes, Graph-based Sybil Detection (GSD) had the greatest attention by both academia and industry. Even though many GSD algorithms exist, there is no analytical framework to reason about their design, especially as they make different assumptions about the used adversary and graph models. In this paper, we bridge this knowledge gap and present a unified framework for systematic evaluation of GSD algorithms. We used this framework to show that GSD algorithms should be designed to find local community structures around known non-Sybil identities, while incrementally tracking changes in the graph as it evolves over time.

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Exploring the design space of social network-based Sybil defenses

Cited by 41 publications

References 20 publications

On the state of OSN-based Sybil defenses

On the state of OSN-based Sybil defenses

On the impact of social botnets for spam distribution and digital-influence manipulation

Graph-based Sybil detection in social and information systems

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