Identity Verification Mechanism for Detecting Fake Profiles in Online Social Networks
Автор: Ali M. Meligy, Hani M. Ibrahim, Mohamed F. Torky
Журнал: International Journal of Computer Network and Information Security(IJCNIS) @ijcnis
Статья в выпуске: 1 vol.9, 2017 года.
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Impersonating users' identity in Online Social Networks (OSNs) is one of the open dilemmas from security and privacy point of view. Scammers and adversaries seek to create set of fake profiles to carry out malicious behaviors and online social crimes in social media. Recognizing the identity of Fake Profiles is an urgent issue of concern to the attention of researchers. In this paper, we propose a detection technique called Fake Profile Recognizer (FPR) for verifying the identity of profiles, and detecting the fake profiles in OSNs. The detection method in our proposed technique is based on utilizing Regular Expression (RE) and Deterministic Finite Automaton (DFA) approaches. We evaluated our proposed detection technique on three datasets types of OSNs: Facebook, Google+, and Twitter. The results explored high Precision, Recall, accuracy, and low False Positive Rates (FPR) of detecting Fake Profiles in the three datasets.
Online Social Networks (OSNs), Security and Privacy, Fake Profiles
Короткий адрес: https://sciup.org/15011739
IDR: 15011739
Список литературы Identity Verification Mechanism for Detecting Fake Profiles in Online Social Networks
- Ellison, Nicole B., "Social network sites: Definition, history, and scholarship", Journal of Computer-Mediated Communication, 13(1) , PP: 210-223, (2007)
- Thomas, G. Jennifer, and S. Aravind, "Predicting Trust and Distrust in Social Networks", SocialCom/PASSAT, (2011).
- L.C.Cutillo., M. Manulis and T. Strufe, "Security and Privacy in Online Social Networks", PP: 512-513. Springer, New York (2010).
- M. Balduzzi, C. Platzer, T. Holz, E. Kirda, D. Balzorotti, and C. Kruegel, "Abusing Social Networks for Automated User Profiling", Research Report RR-10-233,EURECOM,http://www.iseclab.org/papers/socialabuse-TR.pdf, (2012).Accessed 10 Oct 2014.
- Hasib, Abdullah, "Threats of online social networks", International Journal of Computer Science and Network Security (IJCSNS), 9(11), PP: 288-293, (2009).
- Irani, M. Balduzzi, D. Balzarotti, E. Kirda, and C. Pu, "Reverse Social Engineering Attacks in Online Social Networks", Detection of intrusions and malware, and vulnerability assessment. Springer, 6739, PP: 55-74. (2011).
- Z. Yang, C. Wilson, X. Wang, T. Gao, B.Y.Zhao, and Y.Dai," Uncovering Social Network Sybils in the Wild" ACM Transactions on Knowledge Discovery from Data (TKDD), 8(1), (2014).
- G. Wang, T. Konolige, C. Wilson, X. Wang, H. zheng, and Y. Zhao, "You are How You Click: ClickStream Analysis for Sybil Detection", In Usenix Security, PP: 241-256, (2013).
- Q. Cao, M. Sirivianos, X. Yang, and T. Pregueiro," Aiding the Detection of Fake Accounts in Large Scale Social Online Services", Proceedings of 9th USENIX Conference on Networked Systems Design and Implementation, PP: 15-15, (2012).
- B. Viswanath, A. Post, K P. Gummadi, and A. Mislove," An Analysis of Social Networks-Based Sybil Defenses", SIGOMM 10, Proceedings of the ACM SIGCOMM Conference, New York, USA, PP: 363: 374, (2010).
- Kovacs, "Fake" Facebook Friend Request Confirmation",http://news.softpedia.com/news/Fake-Facebook-Friend-Request-Confirmation-Emails-Lead-to-Adobe-Reader-Exploit-375836.shtml,Accessed 23 Oct 2014.
- Meligy, H. M. Ibrahim, and M. Torky. "A Framework for Detecting Cloning Attacks in OSN Based on a Novel Social Graph Topology." MECS, International Journal of Intelligent System and Application, 7(3), PP: 13-20, (2015).
- T. Jean, Y. Feh, " How to Create a Fake Facebook Profile",http://www.wikihow.com/Create-a-Fake-Facebook-Profile,Accessed 23 Oct 2014.
- Protalinski, "Facebook Estimates that Between 5.5% and 11.2% of accounts are fake", http://thenextweb.com/facebook/2014/02/03/facebook-estimates-5-5-11.2-accounts-fake/,Accessed 24 Oct 2014.
- Yu, M. Kaminsky, P B. Gibbons, and A. Flaxman, " SybilGuard: Defending Against Sybil Attacks Via Social Networks", Networking, IEEE/ACM Transactions on, 16(3), PP: 576-589, (2008).
- Yu, P. B. Gibbons, M. Kaminiski, X.Feng, " SybilLimit: A Near-Optimal Social Network Defense against Sybil Attacks", IEEE Symposium on Security and Privacy, 18(22), PP: 2-17, (2008).
- G. Danezis, and P. Mittall, " SybillInfer: Detecting Sybil Nodes Using Social Networks", Network and Distributed System Security Symposium - NDSS, http://libra.msra.cn/Publication/4727139/sybilinfer detecting-Sybil-nodes-using-social-networks, (2009), Accessed 25 Oct 2014.
- M. Fire, G. Katz, and Y. Elovici," Strangers Intrusion Detection Detecting Spammers and Fake Profiles in Social Networks Based on Topology Anomalies", HUMAN, 1(1), pp: 26-39, (2012).
- M.R. Khayyambashi, and F.S. Rizzi, "An Approach for Detecting Profile Cloning in Online Social Networks", 7th International Conference on e-Commerce in Developing Countries with Focus on e-Security (ECDC), IEEE, (2013).
- Gao, J. Hu, and C. Wilson, "Detecting and Characterizing Social Spam Campaigns "Proceedings of the 10th ACM SIGCOMM conference on Internet measurement. ACM, PP: 35-47, (2010).
- R. Baden, N. Spring, and B. Bhattacharjee, "Identifying Close Friends on the Internet", 8th ACM Workshop on Hot Topics in Networks , HotNets, New York, NY, USA,(2009).
- G. Wang, M. Mohanlal, C. Wilson, X. Wang, M. Metzger, H. Zheng, and B.Y. Zhao, "Social Turning Tests: Crowdsourcing Sybil Detection", The 20th Network & Distributed System Security Symposium, NDSS, (2013).
- Lescovec, and A.J. Krevel, "SNAP Datasets: Stanford Large Network Dataset Collection ", http://snap.stanford.edu/data,Accessed 2 Nov 2014.
- Benevenuto F, Magno, G, Rodriguez,T, and Almedia, V, "etecting Spammers on Twitter", In 7th annual Collaboration, Electronic Messaging, Anti-Abuse and, Spam Conference (CEAS) Vol. 6, Redmond, Washington, US, (2010).
- Lee, K, Caverlee, J, and Webb,S. "Uncovering social spammers: social honeypots+ machine learning." Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval. ACM, Geneva Switzerland July 19 - 23, 2010.
