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Volume 30 Issue 6
November  2015
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Jing Jiang, Zi-Fei Shan, Xiao Wang, Li Zhang, Ya-Fei Dai. Understanding Sybil Groups in the Wild[J]. Journal of Computer Science and Technology, 2015, 30(6): 1344-1357. DOI: 10.1007/s11390-015-1602-6
Citation: Jing Jiang, Zi-Fei Shan, Xiao Wang, Li Zhang, Ya-Fei Dai. Understanding Sybil Groups in the Wild[J]. Journal of Computer Science and Technology, 2015, 30(6): 1344-1357. DOI: 10.1007/s11390-015-1602-6
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Understanding Sybil Groups in the Wild

  • 1 State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China;
    2 Department of Computer Science and Technology, Peking University, Beijing 100871, China

Funds: This work is supported by the National Basic Research 973 Program of China under Grant No. 2011CB302305, the National Natural Science Foundation of China under Grant No. 61300006, and the Project of the State Key Laboratory of Software Development Environment under Grant No. SKLSDE-2013ZX-26.
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  • Author Bio:

    Jing Jiang received her B.S. and Ph.D. degrees in computer science from Peking University in 2007 and 2012, respectively. She is now an assistant professor in the State Key Laboratory of Software Development Environment of Beihang University, Beijing. Her research interests include social network, data mining, human factors and social aspects of software engineering.

  • Corresponding author:

    Li Zhang E-mail: lily@buaa.edu.cn

  • Received Date: April 24, 2014
  • Revised Date: January 08, 2015
  • Published Date: November 04, 2015
    Abstract
  • Sybil attacks are one kind of well-known and powerful attacks against online social networks (OSNs). In a sybil attack, a malicious attacker generates a sybil group consisting of multiple sybil users, and controls them to attack the system. However, data confidentiality policies of major social network providers have severely limited researchers' access to large-scale datasets of sybil groups. A deep understanding of sybil groups can provide important insights into the characteristics of malicious behavior, as well as numerous practical implications on the design of security mechanisms. In this paper, we present an initial study to measure sybil groups in a large-scale OSN, Renren. We analyze sybil groups at different levels, including individual information, social relationships, and malicious activities. Our main observations are: 1) user information in sybil groups is usually incomplete and in poor quality; 2) sybil groups have special evolution patterns in connectivity structure, including bursty actions to add nodes, and a monotonous merging pattern that lacks non-singleton mergings; 3) several sybil groups have strong relationships with each other and compose sybil communities, and these communities cover a large number of users and pose great potential threats; 4) some sybil users are not banned until a long time after registration in some sybil groups. The characteristics of sybil groups can be leveraged to improve the security mechanisms in OSNs to defend against sybil attacks. Specifically, we suggest that OSNs should 1) check information completeness and quality, 2) learn from dynamics of community connectivity structure to detect sybil groups, 3) monitor sybil communities and inspect them carefully to prevent collusion, and 4) inspect sybil groups that behave normally even for a long time to prevent potential malicious behaviors.
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