Guided socialbots: Infiltrating the social networks of specific organizations' employees

Aviad Elyashar, Michael Fire, Dima Kagan, Yuval Elovici

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

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.

Original languageEnglish
Pages (from-to)87-106
Number of pages20
JournalAI Communications
Volume29
Issue number1
DOIs
StatePublished - 29 Jan 2016

Keywords

  • Facebook
  • Socialbots
  • Xing
  • organization mining
  • social networks security and privacy

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