Improving Accuracy of Sybil Account Detection in OSNs by Leveraging Victim Prediction

  • Qingqing Zhou
  • Zhigang Chen
  • Rui Huang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 768)


The rapid development of social networks, led to a variety of abnormal accounts of the increasingly rampant, and one of the most representative is Sybil. They will create a variety of malicious activities, which seriously endanger the social network and user security. For Sybil account detection this problem, we propose a very efficient Sybil account detection model, which leverages victim prediction to improve the detection accuracy. First, given the exacted features, we design a classifier for victim prediction. Then, prediction results are applied to the social network graph model to modify the weight of the edge. Next, a modified random walk algorithm is used for trust propagation. Finally we rank all nodes according their trust value. And our detection model guarantees that most normal accounts rank higher than Sybil accounts so that operators of online social networks can take actions against low-ranking Sybil accounts.


Sybil account detection Social networks Victim prediction 



This work is supported by the National Natural Science Foundation of China (Grant No. 71633006, Grant No. 61672540), and China Postdoctoral Science Foundation funded project (Grant No. 2017M612586), and Central South University students innovation and entrepreneurship project (Grant No. 201710533511).


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Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.School of SoftwareCentral South UniversityChangshaPeople’s Republic of China

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