Iranian Society of Cryptology The ISC International Journal of Information Security 2008-2045 11 2 2019 07 01 Detection of Fake Accounts in Social Networks Based on One Class Classification 173 183 91325 10.22042/isecure.2019.165312.450 EN Mohammad Reza Mohammadrezaei Department of Computer, Borujerd Branch, Islamic Azad University, Borujerd, Iran Mohammad Ebrahim Shiri Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran Amir Masoud Rahmani Computer engineering department, Science and Research Branch, Islamic Azad University, Tehran, Iran Journal Article 2019 01 02 Detection of fake accounts on social networks is a challenging process. The previous methods in identification of fake accounts have not considered the strength of the users’ communications, hence reducing their efficiency. In this work, we are going to present a detection method based on the users’ similarities considering the network communications of the users. In the first step, similarity measures somethings such as common neighbors, common neighbors graph edges, cosine, and the Jaccard similarity coefficient are calculated based on adjacency matrix of the corresponding graph of the social network. In the next step, in order to reduce the complexity of data, Principal Component Analysis is applied to each computed similarity matrix to provide a set of informative features. then, a set of highly informative eigenvectors are selected using elbow-method. Extracted features are employed to train a One Class Classification (OCC) algorithm. Finally, this trained model is employed to identify fake accounts. As our experimental results indicate the promising performance of the proposed method a detection accuracy and false negative rates are 99.6% and 0%, respectively. We conclude that bringing similarity measures and One Class Classification algorithms into play, rather than the multi-class algorithms, provide better results. https://www.isecure-journal.com/article_91325_ec08e7358dc25608363621b4205fbfb1.pdf