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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)

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.

Keywords

Sybil account detection Social networks Victim prediction 

Notes

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).

References

  1. 1.
    Gao, H., Hu, J., Huang, T., Wang, J.: Security issues in online social networks. IEEE Internet Comput. 15, 56–63 (2011)CrossRefGoogle Scholar
  2. 2.
    Fire, M., Goldschmidt, R., Elovici, Y.: Online social networks: threats and solutions. IEEE Commun. Surv. Tutor. 16, 2019–2036 (2013)CrossRefGoogle Scholar
  3. 3.
    Caviglione, L., Coccoli, M., Merlo, A.: A taxonomy-based model of security and privacy in online social networks. Int. J. Comput. Sci. Eng. 9, 325–338 (2014)CrossRefGoogle Scholar
  4. 4.
    Zhang, D., Chen, Z., Chen, L., Shen, X.: Energy-balanced cooperative transmission based on relay selection and power control in energy harvesting wireless sensor network. Comput. Netw. 104, 189–197 (2016)CrossRefGoogle Scholar
  5. 5.
    Thomas, K., Mccoy, D., Grier, C., Kolcz, A., Paxson, V.: Trafficking fraudulent accounts: the role of the underground market in Twitter spam and sbuse. In: USENIX Conference on Security, Washington, USA, pp. 195–210 (2014)Google Scholar
  6. 6.
    Huang, T.K., Rahman, M.S., Madhyastha, H.V., Faloutsos, M., Ribeiro, B.: An analysis of socware cascades in online social networks. In: WWW, pp. 619–630 (2013)Google Scholar
  7. 7.
    Thomas, K., Grier, C., Song, D., Paxson, V.: Suspended accounts in retrospect: an analysis of twitter spam. In: Proceedings of IMC, pp. 243–258 (2011)Google Scholar
  8. 8.
    Boshmaf, Y., Muslukhov, I., Beznosov, K., Ripeanu, M.: The socialbot network: when bots socialize for fame and money. In: Twenty-Seventh Computer Security Applications Conference, ACSAC 2011, Orlando, FL, USA, pp. 93–102 (2011)Google Scholar
  9. 9.
    Douceur, J.R.: The sybil attack. In: Druschel, P., Kaashoek, F., Rowstron, A. (eds.) IPTPS 2002. LNCS, vol. 2429, pp. 251–260. Springer, Heidelberg (2002). doi: 10.1007/3-540-45748-8_24 CrossRefGoogle Scholar
  10. 10.
    Bilge, L., Strufe, T., Balzarotti, D., Kirda, E.: All your contacts are belong to us: automated identity theft attacks on social networks. In: International Conference on World Wide Web, WWW 2009, Madrid, Spain, pp. 551–560 (2009)Google Scholar
  11. 11.
    Zhang, D., Chen, Z.: Energy-efficiency of cooperative communication with guaranteed E2E reliability in WSNs. Int. J. Distrib. Sensor Netw. 2013, 94–100 (2013)Google Scholar
  12. 12.
    Gong, N.Z., Frank, M., Mittal, P.: SybilBelief: a semi-supervised learning approach for structure-based sybil detection. IEEE Trans. Inf. Forensics Secur. 9, 976–987 (2013)CrossRefGoogle Scholar
  13. 13.
    Cao, Q., Sirivianos, M., Yang, X., Pregueiro, T.: Aiding the detection of fake accounts in large scale social online services. In: USENIX Conference on Networked Systems Design and Implementation, p. 15 (2012)Google Scholar
  14. 14.
    Wei, W., Xu, F., Tan, C.C., Li, Q.: SybilDefender: defend against Sybil attacks in large social networks. In: Proceedings - IEEE INFOCOM, vol. 131, pp. 1951–1959 (2012)Google Scholar
  15. 15.
    Tran, N., Li, J., Subramanian, L.: Optimal Sybil-resilient node admission control. In: IEEE International Conference on Computer Communications, Joint Conference of the IEEE Computer and Communications Societies, INFOCOM 2011, Shanghai, China, pp. 3218–3226 (2011)Google Scholar
  16. 16.
    Tran, N., Min, B., Li, J.: Sybil-resilient online content voting. In: USENIX Symposium on Networked Systems Design and Implementation, NSDI 2009, Boston, MA, USA, pp. 15–28 (2009)Google Scholar
  17. 17.
    Danezis, G., Mittal, P.: SybilInfer: detecting Sybil nodes using social networks. In: Network and Distributed System Security Symposium, NDSS 2009, San Diego, California, USA (2009)Google Scholar
  18. 18.
    Yu, H., Gibbons, P.B., Kaminsky, M.: SybilLimit: a near-optimal social network defense against Sybil attacks. In: IEEE Symposium on Security and Privacy, SP 2008, NDSS 2008, pp. 3–17. IEEE (2008)Google Scholar
  19. 19.
    Yu, H., Kaminsky, M., Gibbons, P.B., Flaxman, A.D.: SybilGuard: defending against Sybil attacks via social networks. IEEE/ACM Trans. Netw. 16, 576–589 (2008)CrossRefGoogle Scholar
  20. 20.
    Mohaisen, A., Yun, A., Kim, Y.: Measuring the mixing time of social graphs. In: Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement, Melbourne, Australia, pp. 383–389 (2010)Google Scholar
  21. 21.
    Koll, D., Li, J., Stein, J., Fu, X.: On the effectiveness of sybil defenses based on online social networks. In: IEEE International Conference on Network Protocols, pp. 1–2 (2013)Google Scholar
  22. 22.
    Zhang, Y., Lv, S., Fan, D.: Anomaly detection in online social networks. Chin. J. Comput. 38, 2011–2027 (2015)Google Scholar
  23. 23.
    Boshmaf, Y., Logothetis, D., Siganos, G., Leria, J., Lorenzo, J.: Íntegro: leveraging victim prediction for robust fake account detection in OSNs. In: Proceedings of the 22nd Annual Network and Distributed System Security Symposium, San Diego, USA (2015)Google Scholar
  24. 24.
    Leskovec, J., Faloutsos, C.: Sampling from large graphs. In: Proceedings of the ACM SIGKDD Conference, pp. 631–636 (2006)Google Scholar
  25. 25.
    Hastie, T., Tibshirani, R., Friedman, J.: The elements of statistical learning: data mining, inference, and prediction. Math. Intell. 27, 83–85 (2009)MATHGoogle Scholar

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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