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You Are How You Click: Clickstream Analysis for Sybil Detection

Authors: 

Gang Wang and Tristan Konolige, University of California, Santa Barbara; Christo Wilson, Northeastern University; Xiao Wang, Renren Inc.; Haitao Zheng and Ben Y. Zhao, University of California, Santa Barbara

Abstract: 

Fake identities and Sybil accounts are pervasive in today’s online communities. They are responsible for a growing number of threats, including fake product reviews, malware and spam on social networks, and astroturf political campaigns. Unfortunately, studies show that existing tools such as CAPTCHAs and graph-based Sybil detectors have not proven to be effective defenses.

In this paper, we describe our work on building a practical system for detecting fake identities using server-side clickstream models. We develop a detection approach that groups “similar” user clickstreams into behavioral clusters, by partitioning a similarity graph that captures distances between clickstream sequences. We validate our clickstream models using ground-truth traces of 16,000 real and Sybil users from Renren, a large Chinese social network with 220M users. We propose a practical detection system based on these models, and show that it provides very high detection accuracy on our clickstream traces. Finally, we worked with collaborators at Renren and LinkedIn to test our prototype on their server-side data. Following positive results, both companies have expressed strong interest in further experimentation and possible internal deployment.

Gang Wang, University of California, Santa Barbara

Tristan Konolige, University of California, Santa Barbara

Christo Wilson, Northeastern University

Xiao Wang, Renren Inc.

Haitao Zheng, University of California, Santa Barbara

Ben Y. Zhao, University of California, Santa Barbara

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BibTeX
@inproceedings {180370,
author = {Gang Wang and Tristan Konolige and Christo Wilson and Xiao Wang and Haitao Zheng and Ben Y. Zhao},
title = {You Are How You Click: Clickstream Analysis for Sybil Detection},
booktitle = {22nd USENIX Security Symposium (USENIX Security 13)},
year = {2013},
isbn = {978-1-931971-03-4},
address = {Washington, D.C.},
pages = {241--256},
url = {https://www.usenix.org/conference/usenixsecurity13/technical-sessions/presentation/wang},
publisher = {USENIX Association},
month = aug,
}
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