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http://dx.doi.org/10.3745/KTSDE.2015.4.3.129

Unsupervised Scheme for Reverse Social Engineering Detection in Online Social Networks  

Oh, Hayoung (숭실대학교 정보통신전자공학부)
Publication Information
KIPS Transactions on Software and Data Engineering / v.4, no.3, 2015 , pp. 129-134 More about this Journal
Abstract
Since automatic social engineering based spam attacks induce for users to click or receive the short message service (SMS), e-mail, site address and make a relationship with an unknown friend, it is very easy for them to active in online social networks. The previous spam detection schemes only apply manual filtering of the system managers or labeling classifications regardless of the features of social networks. In this paper, we propose the spam detection metric after reflecting on a couple of features of social networks followed by analysis of real social network data set, Twitter spam. In addition, we provide the online social networks based unsupervised scheme for automated social engineering spam with self organizing map (SOM). Through the performance evaluation, we show the detection accuracy up to 90% and the possibility of real time training for the spam detection without the manager.
Keywords
Online Social Networks; Reverse Social Engineering; Unsupervised Scheme;
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  • Reference
1 Sophos Facebook ID Probe. http://www.sophos.com/pressoffice/news/articles/2007/08/facebook.html, 2008.
2 D. Irani, M. Balduzzi, D. Balzarotti, E. Kirda, and C. Pu, "Reverse social engineering attacks in online social networks," in Detection of Intrusions and Malware, and Vulnerability Assessment, ed: Springer, pp.55-74, 2011.
3 Jagatic, T. N., Johnson, N. A., Jakobsson, M., and Menczer, F. Social phishing. Commun. ACM, Vol.50, No.10, pp.94-100, 2007.   DOI
4 J. M. Gomez Hidalgo, G. C. Bringas, E. P. Sanz, and F. C. Garcia, "Content based SMS spam filtering," in Proceedings of the 2006 ACM symposium on Document engineering, pp.107-114, 2006.
5 G. V. Cormack, J. M. Gomez Hidalgo, and E. P. Sanz, "Spam filtering for short messages," in Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, pp.313-320, 2007.
6 Liu JY, Zhao YH, and Zhang ZX et al. "Spam short messages detection via mining social netwoks," JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, Vol.27, No.3, pp.506-514, May, 2012. DOI 10.1007/s11390-012-1239-7   DOI
7 Richard Bassett et al., "DATA MINING AND SOCIAL NETWORKING SITES: PROTECTING BUSINESS INFRASTRUCTURE AND BEYOND," Issues in Information Systems, Vol.XI, No.1, 2010.
8 Mariam Adedoyin-Olowe, Mohamed Medhat Gaber, and Frederic Stahl, "A Survey of Data Mining Techniques for Social Network Analysis," Cornell University.
9 Kurt Thomas, Chris Grier, Vern Paxson, and Dawn Song, "Suspended Accounts in Retrospect: An Analysis of Twitter Spam," Internet Measurement Conference(IMC), 2011.
10 Meng Jiang, Peng Cui, Alex Beutel, Christos Faloutsos, and Shiqiang Yang, "CatchSync : Catching Synchronized Behavior in Large Directed Graphs," KDD '14
11 David Easley, Jon Kleinberg, "Networks, Crowds, and Markets: Reasoning About a Highly Connected World," Cambridge University Press.
12 Neil Zhenqiang Gong, Mario Frank, and Prateek Mittal, "SybilBelief: A Semi-supervised Learning Approach for Structure-based Sybil Detection," IEEE Transactions on Information Forensics and Security, Vol.9, No.6, 2014.
13 Qiang Cao, Xiaowei Yang, Jieqi Yu, and Christopher Palow, "Uncovering Large Groups of Active Malicious Accounts in Online Social Networks," ACM CCS 2014
14 Hayoung Oh, Jiyoung Lim, Kijoon Chae and Jungchan Nah, "Home Gateway with Automated Real-Time Intrusion Detection for Secure Home Networks," Computational Science and Its Application-ICCSA 2006 Lecture Notes in Computer Science, Vol.3983, pp.440-447, 2006.
15 Kyoungae Hwang, Hayoung Oh, Jiyoung Lim, Kijoon Chae, and Jungchan Nah, "Traffic Attributes Correlation Mechanism based on Self-Organizing Maps for Real-Time Intrusion Detection," Information Processing Society Journal, Oct., 2005.