Unsupervised Scheme for Reverse Social Engineering Detection in Online Social Networks


KIPS Transactions on Software and Data Engineering, Vol. 4, No. 3, pp. 129-134, Mar. 2015
10.3745/KTSDE.2015.4.3.129,   PDF Download:

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.


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Cite this article
[IEEE Style]
H. Y. Oh, "Unsupervised Scheme for Reverse Social Engineering Detection in Online Social Networks," KIPS Transactions on Software and Data Engineering, vol. 4, no. 3, pp. 129-134, 2015. DOI: 10.3745/KTSDE.2015.4.3.129.

[ACM Style]
Ha Young Oh. 2015. Unsupervised Scheme for Reverse Social Engineering Detection in Online Social Networks. KIPS Transactions on Software and Data Engineering, 4, 3, (2015), 129-134. DOI: 10.3745/KTSDE.2015.4.3.129.