Fake Profiles Types of Online Social Networks: A Survey

  • Authors

    • Rafeef Kareem
    • Wesam Bhaya
    2018-11-27
    https://doi.org/10.14419/ijet.v7i4.19.28071
  • Online Social Networks, OSNs, Fake Profile, Fake Account.
  • Today, OSNs (Online Social Networks) considered the most platforms common on the Internet. It plays a substantial role for users of the internet to hold out their everyday actions such as news reading, content sharing, product reviews, messages posting, and events discussing etc. Unfortunately, on the OSNs some new attacks have been recognized. Different types of spammers are existing in these OSNs. These cyber-criminals containing online fraudsters, sexual predators, catfishes, social bots, and advertising campaigners etc.

    OSNs abuse in different ways especially by creating fake profiles to carry out scams and spread their content. The identities of all these malicious are so damaging to the service providers and the users. From the opinion of OSNs service providers, the loss of bandwidth moreover the overall reputation of the network is affected by fake profiles. Thus, needing more complex automated methods, and tremendous effort manpower to discover and stopping these harmful users.

    This paper explains different kinds of OSNs risk generators such as cloned profiles, compromised profiles, and online bots (spam-bots, chat-bots, and social-bots). In addition, it presents several classifications of features that have been used for training classifiers in order to discover fake profiles. We try to show different ways that used to detect every kind of these malicious profiles. Also, this paper trying to show what is the dangerous type of profile attacks and the most popular in OSNs.

     

     


  • References

    1. [1] M. Egele, C. Kruegel, and G. Vigna, “C OMPA : Detecting Compromised Accounts on Social Networks,†NDSS Sympoium, 2013.

      [2] Y. Boshmaf, I. Muslukhov, K. Beznosov, and M. Ripeanu, “Design and analysis of a social botnet,†Comput. Networks, vol. 57, pp. 556–578, 2012.

      [3] L. Bilge, T. Strufe, D. Balzarotti, and E. Kirda, “All your contacts are belong to us,†Proc. 18th Int. Conf. World wide web - WWW ’09, p. 551, 2009.

      [4] M. A. Wani and S. Jabin, “A sneak into the Devil’s Colony - Fake Profiles in Online Social Networks,†eprint arXiv:1705.09929, 2017.

      [5] C. VanDam, J. Tang, and P.-N. Tan, “Understanding compromised accounts on Twitter,†Proc. Int. Conf. Web Intell. - WI ’17, pp. 737–744, 2017.

      [6] M. Y. Kharaji and F. S. Rizi, “An IAC Approach for Detecting Profile Cloning in Online Social Networks,†Int. J. Netw. Secur. Its Appl. (IJNSA), vol. 6, no. 1, pp. 75–90, 2014.

      [7] T. Stein, E. Chen, and K. Mangla, “Facebook immune system,†Proc. 4th Work. Soc. Netw. Syst. - SNS ’11, vol. m, pp. 1–8, 2011.

      [8] X. Zheng, Y. M. Lai, K. P. Chow, L. C. K. Hui, and S. M. Yiu, “Sockpuppet detection in online discussion forums,†Proc. - 7th Int. Conf. Intell. Inf. Hiding Multimed. Signal Process. IIHMSP 2011, pp. 374–377, 2011.

      [9] P. Gao, N. Z. Gong, S. Kulkarni, K. Thomas, and P. Mittal, “SybilFrame: A Defense-in-Depth Framework for Structure-Based Sybil Detection,†Comput. Res. Repos., p. 17, 2015.

      [10] J. R. Douceur, “The Sybil Attack,†Springer-Verlag London, UK, IPTPS '01 Revised Papers from the First International Workshop on Peer-to-Peer Systems pp. 251–260, 2002.

      [11] B. Viswanath, A. Post, K. P. Gummadi, and A. Mislove, “An analysis of social network-based Sybil defenses,†Proc. ACM SIGCOMM 2010 Conf. SIGCOMM - SIGCOMM ’10, p. 363, 2010.

      [12] Z. Yang, C. Wilson, X. Wang, T. Gao, B. Y. Zhao, and Y. Dai, “Uncovering Social Network Sybils in the Wild,†Internet Meas. Conf., vol. 8, no. 1, 2011.

      [13] Y. Boshmaf, I. Muslukhov, K. Beznosov, and M. Ripeanu, “The socialbot network: when bots socialize for fame and money,†Acm, p. 93, 2011.

      [14] B. A. Shawar and E. Atwell, “Measurement and Classification of Humans and Bots in Internet Chat,†Bridg. Gap Acad. Ind. Res. Dialog Technol. Work. Proc., no. August, pp. 89–96, 2007.

      [15] F. Salehi Rizi et al., “A New Approach for Finding Cloned Profiles in Online Social Networks,†arXiv Prepr. arXiv1406.7377, vol. 6, no. April, pp. 25–37, 2014.

      [16] G. Kontaxis, I. Polakis, S. Ioannidis, and E. P. Markatos, “Detecting social network profile cloning,†2011 IEEE Int. Conf. Pervasive Comput. Commun. Work. PERCOM Work. 2011, pp. 295–300, 2011.

      [17] H. Yu et al., “SybilGuard,†Proc. 2006 Conf. Appl. Technol. Archit. Protoc. Comput. Commun. - SIGCOMM ’06, vol. pages, no. 3, p. 267, 2006.

      [18] D. Savage, X. Zhang, X. Yu, P. Chou, and Q. Wang, “Anomaly detection in online social networks,†Soc. Networks, vol. 39, no. 1, pp. 62–70, 2014.

      [19] A. Wang, “Detecting spam bots in online social networking sites: a machine learning approach,†Data Appl. Secur. Priv. XXIV, pp. 335–342, 2010.

      [20] Z. Bu, Z. Xia, and J. Wang, “A sock puppet detection algorithm on virtual spaces,†Knowledge-Based Syst., vol. 37, pp. 366–377, 2013.

      [21] T. Solorio, R. Hasan, and M. Mizan, “A Case Study of Sockpuppet Detection in Wikipedia,†Proc. Work. Lang. Anal. Soc. Media, no. Lasm, pp. 59–68, 2013.

      [22] B. Bhumiratana, “A model for automating persistent identity clone in online social network,†Proc. 10th IEEE Int. Conf. Trust. Secur. Priv. Comput. Commun. Trust. 2011, 8th IEEE Int. Conf. Embed. Softw. Syst. ICESS 2011, 6th Int. Conf. FCST 2011, pp. 681–686, 2011.

      [23] D. Chen, L. Lü, M. S. Shang, Y. C. Zhang, and T. Zhou, “Identifying influential nodes in complex networks,†Phys. A Stat. Mech. its Appl., vol. 391, no. 4, pp. 1777–1787, 2012.

      [24] B. Wang, L. Zhang, and N. Z. Gong, “SybilSCAR: Sybil detection in online social networks via local rule based propagation,†IEEE INFOCOM 2017 - IEEE Conf. Comput. Commun., no. May, pp. 1–9, 2017.

      [25] B. Erşahin, Ö. Aktaş, D. Kilmç, and C. Akyol, “Twitter fake account detection,†2nd Int. Conf. Comput. Sci. Eng. UBMK 2017, pp. 388–392, 2017.

      [26] A. El Azab, A. M. Idrees, M. A. Mahmoud, and H. Hefny, “Fake Account Detection in Twitter Based on Minimum Weighted Feature set,†Int. J. Comput. Electr. Autom. Control Inf. Eng., vol. 10, no. 1, pp. 13–18, 2016.

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    Kareem, R., & Bhaya, W. (2018). Fake Profiles Types of Online Social Networks: A Survey. International Journal of Engineering & Technology, 7(4.19), 919-925. https://doi.org/10.14419/ijet.v7i4.19.28071