International Conference on Knowledge Management in Organizations

KMO 2015: Knowledge Management in Organizations pp 602-617 | Cite as

Intelligent Sybil Attack Detection on Abnormal Connectivity Behavior in Mobile Social Networks

Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 224)

Abstract

There have been a large number of researches on mobile networks in the literature, focusing on a variety of secured applications over the network, including the use of their connections, fake identification and attacks on social group. These applications are created for the intention to collect confidential information, money laundering, blackmailing and to perform other crime activity. The purpose of this research is to identify the behavior of the honest node (network account) and fake node (network account) on mobile social network.

In this research, the behavior survey of these nodes is carried out and further analysed with the help of graph-based Sybil detection system. This paper particularly studies Sybil attacks and its defense system for IoT (Internet-of-Things) environment. To be implied, the identification of each forged Sybil node is to be tracked on the basis of nodes connectivity and their timing of connectivity as well as frequency among each other. Sybil node has a forged identity in different locations and also reports its virtual location information to servers.

Keywords

Sybil attack Mobile social network Anomaly detection 

References

  1. Alvisi, L., Clement, A., Epasto, A., Lattanzi, S., Panconesi, A.,: SoK: the evolution of sybil defense via social networks : security and privacy (SP), 2013: In: IEEE Symposium on security and privacy, pp. 382–396 (2013)Google Scholar
  2. Jyothi, B.S., Janakiram, D.: SyMon: a practical approach to defend large structured P2P systems against Sybil Attack. Peer-To-Peer Networking and Applications 4(3), 289–308 (2011)CrossRefGoogle Scholar
  3. Cortimiglia, M.N., Renga, F., Ghezzi, A.: Mobile social networking: a case study in an australian mobile network operator, mobile business (ICMB). In: 2011 Tenth International Conference on Mobile Business, pp. 84–92 (2011)Google Scholar
  4. Haifeng, Y., Gibbons, P.B., Kaminsky, M., Feng, X.: SybilLimit: a near-optimal social network defense against sybil attacks. IEEE/ACM Trans. Netw. 18(3), 885–898 (2010)CrossRefGoogle Scholar
  5. Haifeng, Y., Kaminsky, M., Gibbons, P.B., Flaxman, A.D.: SybilGuard: defending against sybil attacks via social networks. IEEE/ACM Netw. 16(3), 576–589 (2008)CrossRefGoogle Scholar
  6. Hao, X., Weidong, X., Tang, D., Tang, J., Wang, Z.: Core-based community evolution in mobile social networks : big data, 2013. In: IEEE International Conference (2013)Google Scholar
  7. Jing, J., Zifei, S., Wenpeng, S., Xiao, W., Yafei, D.: Detecting and validating sybil groups in the wild, distributed computing systems workshops (ICDCSW). In: 2012 32nd International Conference, pp. 127–132 (2012)Google Scholar
  8. Koleshwar, A., Thakare, V., Sherekar, S.: Study of mobile cloud computing security against cyber attacks. Int. J. Adv. Res. Comput. Sci. 5(4) 187–191 (2014)Google Scholar
  9. Kuan, Z., Xiaohui, L., Rongxing, L., Xuemin, S.: SAFE: a social based updatable filtering protocol with privacy-preserving in mobile social networks, communications (ICC). In: IEEE International Conference, pp. 6045–6049 (2013)Google Scholar
  10. Kuan, Z., Xiaohui, L., Rongxing, L., Xuemin, S.: Sybil attacks and their defenses in the internet of things. Internet Things J. IEEE 1(5), 372–383 (2014)CrossRefGoogle Scholar
  11. Mei, A., Stefa, J.: Give2Get: forwarding in social mobile wireless networks of selfish individuals. IEEE Trans. Dependable Secure Comput. 9(4), 569–582 (2012)CrossRefGoogle Scholar
  12. Mohaien, A., Kune, D.F.: Secure encounter-based mobile social networks: requirements, designs, and tradeoffs. IEEE Trans. Dependable Secure Comput. 10(6), 380–393 (2013)CrossRefGoogle Scholar
  13. Pengfei, L., Xiaohan, W., Che, X., Chen Z., Gu, Y.: Defense against sybil attacks in directed social networks. In: 2014 19th International Conference Digital Signal Processing (DSP) (2014)Google Scholar
  14. Quercia, D., Hailes S.: Sybil attacks against mobile users: friends and foes to the rescue. In: 2010 Proceedings IEEE, INFOCOM (2010)Google Scholar
  15. Shrivastava, N., Majumder, A., Rastogi, R.: Mining (social) network graphs to detect random link attacks. In: IEEE 24th International Conference on Data Engineering, ICDE, pp. 486–495c (2008)Google Scholar
  16. Wei, C., Jie, W., Tan, C.C., Feng, L.: Sybil defenses in mobile social networks,: In: 2013 IEEE Global Communications Conference (GLOBECOM), pp. 641–646 (2013)Google Scholar
  17. Wei, W., Fengyuan, X., Tan, C.C., Qun, L.: SybilDefender: defend against sybil attacks in large social networks, In: 2012 Proceedings IEEE, INFOCOM, pp. 1951–1959(2012)Google Scholar
  18. Xu, G., Zhang, Y., Zhou, X.: Discovering task-oriented usage pattern for web recommendation, In: Sevents Australiasian Database Conference (ADC), (CRPIT), vol. 49 (2006)Google Scholar
  19. Yan, S., Lihua, Y., Wenmao, L.: Defending sybil attacks in mobile social networks. In: 2014 IEEE Conference Computer Communications Workshops (INFOCOM WKSHPS), pp. 163–164 (2014)Google Scholar
  20. Yuhang, Z., Zhaoxiang, Z., Yunhong, W., Jianyun, L.: Robust mobile spamming detection via graph patterns. In: 2012 21st International Conference Pattern Recognition (ICPR), pp. 127–132 (2012)Google Scholar
  21. Zhang, B., Wang, Y., Vasilakos, A.V., Ma, J.: Mobile social networking: reconnect virtual community with physical space. Telecommun. Syst. 54(1), 91–110 (2013)CrossRefGoogle Scholar
  22. Zhang, H., Dantu, R.: Predicting social ties in mobile phone networks, intelligence and security informatics (ISI). In: 2010 IEEE International Conference, pp. 25–30 (2010)Google Scholar
  23. Zhang, H., Dantu, R., Cangussu, J.W.: Socioscope: human relationship and behavior analysis in social networks. IEEE Trans. Syst. Man Cybern. Part A: Syst. Hum. 41(6), 1122–1143 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Advanced Analytics InstituteUniversity of Technology SydneyUltimoAustralia

Personalised recommendations