IPTPS '01 Revised Papers from the First International Workshop on Peer-to-Peer Systems
The sybil attack in sensor networks: analysis & defenses
Proceedings of the 3rd international symposium on Information processing in sensor networks
SybilGuard: defending against sybil attacks via social networks
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
An Empirical Study of Collusion Behavior in the Maze P2P File-Sharing System
ICDCS '07 Proceedings of the 27th International Conference on Distributed Computing Systems
Low-resource routing attacks against tor
Proceedings of the 2007 ACM workshop on Privacy in electronic society
SybilLimit: A Near-Optimal Social Network Defense against Sybil Attacks
SP '08 Proceedings of the 2008 IEEE Symposium on Security and Privacy
Spamalytics: an empirical analysis of spam marketing conversion
Proceedings of the 15th ACM conference on Computer and communications security
User interactions in social networks and their implications
Proceedings of the 4th ACM European conference on Computer systems
Sybil-resilient online content voting
NSDI'09 Proceedings of the 6th USENIX symposium on Networked systems design and implementation
Characterizing user behavior in online social networks
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
An analysis of social network-based Sybil defenses
Proceedings of the ACM SIGCOMM 2010 conference
WOSN'10 Proceedings of the 3rd conference on Online social networks
@spam: the underground on 140 characters or less
Proceedings of the 17th ACM conference on Computer and communications security
Detecting and characterizing social spam campaigns
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Understanding latent interactions in online social networks
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Measuring the mixing time of social graphs
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Detecting spammers on social networks
Proceedings of the 26th Annual Computer Security Applications Conference
Proceedings of the 4th Workshop on Social Network Systems
Dirty jobs: the role of freelance labor in web service abuse
SEC'11 Proceedings of the 20th USENIX conference on Security
Suspended accounts in retrospect: an analysis of twitter spam
Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
Uncovering social network sybils in the wild
Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
Serf and turf: crowdturfing for fun and profit
Proceedings of the 21st international conference on World Wide Web
Aiding the detection of fake accounts in large scale social online services
NSDI'12 Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation
You are how you click: clickstream analysis for Sybil detection
SEC'13 Proceedings of the 22nd USENIX conference on Security
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Sybil accounts are fake identities created to unfairly increase the power or resources of a single malicious user. Researchers have long known about the existence of Sybil accounts in online communities such as file-sharing systems, but they have not been able to perform large-scale measurements to detect them or measure their activities. In this article, we describe our efforts to detect, characterize, and understand Sybil account activity in the Renren Online Social Network (OSN). We use ground truth provided by Renren Inc. to build measurement-based Sybil detectors and deploy them on Renren to detect more than 100,000 Sybil accounts. Using our full dataset of 650,000 Sybils, we examine several aspects of Sybil behavior. First, we study their link creation behavior and find that contrary to prior conjecture, Sybils in OSNs do not form tight-knit communities. Next, we examine the fine-grained behaviors of Sybils on Renren using clickstream data. Third, we investigate behind-the-scenes collusion between large groups of Sybils. Our results reveal that Sybils with no explicit social ties still act in concert to launch attacks. Finally, we investigate enhanced techniques to identify stealthy Sybils. In summary, our study advances the understanding of Sybil behavior on OSNs and shows that Sybils can effectively avoid existing community-based Sybil detectors. We hope that our results will foster new research on Sybil detection that is based on novel types of Sybil features.