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

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Theoretical Computer Science (NCTCS 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 768))

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Abstract

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.

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Acknowledgement

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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Authors and Affiliations

  1. School of Software, Central South University, Changsha, Hu’nan Province, People’s Republic of China

    Qingqing Zhou, Zhigang Chen & Rui Huang

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  1. Qingqing Zhou
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  2. Zhigang Chen
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  3. Rui Huang
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Corresponding author

Correspondence to Zhigang Chen .

Editor information

Editors and Affiliations

  1. The University of Texas at Dallas, Richardson, Texas, USA

    Dingzhu Du

  2. Hefei University of Technology, Hefei, Anhui Province, China

    Lian Li

  3. National University of Defense Technology, Changsha, Hunan, China

    En Zhu

  4. Huazhong University of Science and Technology, Wuhan, Hubei, China

    Kun He

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Zhou, Q., Chen, Z., Huang, R. (2017). Improving Accuracy of Sybil Account Detection in OSNs by Leveraging Victim Prediction. In: Du, D., Li, L., Zhu, E., He, K. (eds) Theoretical Computer Science. NCTCS 2017. Communications in Computer and Information Science, vol 768. Springer, Singapore. https://doi.org/10.1007/978-981-10-6893-5_14

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  • DOI: https://doi.org/10.1007/978-981-10-6893-5_14

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6892-8

  • Online ISBN: 978-981-10-6893-5

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