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Authors: Elyashar, Aviad | Fire, Michael | Kagan, Dima | Elovici, Yuval

Article Type: Research Article

Abstract: A dimension of the Internet that has gained great popularity in recent years is the platform of online social networks (OSNs). Users all over the world write, share, and publish personal information about themselves, their friends, and their workplaces within this platform of communication. In this study we demonstrate the relative ease of creating malicious socialbots that act as social network “friends”, resulting in OSN users unknowingly exposing potentially harmful information about themselves and their places of employment. We present an algorithm for infiltrating specific OSN users who are employees of targeted organizations, using the topologies of organizational social networks …and utilizing socialbots to gain access to these networks. We focus on two well-known OSNs – Facebook and Xing – to evaluate our suggested method for infiltrating key-role employees in targeted organizations. The results obtained demonstrate how adversaries can infiltrate social networks to gain access to valuable, private information regarding employees and their organizations. Show more

Keywords: Socialbots, social networks security and privacy, organization mining, Facebook, Xing

DOI: 10.3233/AIC-140650

Citation: AI Communications, vol. 29, no. 1, pp. 87-106, 2016