Proactive insider threat detection through social media: the YouTube case

Authors:
Miltiadis Kandias;Vasilis Stavrou;Nick Bozovic;Dimitris Gritzalis
Affiliations:
Athens University of Economics and Business, Athens, Greece;Athens University of Economics and Business, Athens, Greece;Athens University of Economics and Business, Athens, Greece;Athens University of Economics and Business, Athens, Greece
Venue:
Proceedings of the 12th ACM workshop on Workshop on privacy in the electronic society
Year:
2013

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Abstract

Insider threat is a major issue in cyber and corporate security. In this paper we study the psychosocial perspective of the insider via social media, Open Source Intelligence, and user generated content classification. Inductively, we propose a prediction method by evaluating the predisposition towards law enforcement and authorities, a personal psychosocial trait closely connected to the manifestation of malevolent insiders. We propose a methodology to detect users holding a negative attitude towards authorities. For doing so we facilitate the use of machine learning techniques and of a dictionary-based approach, so as to detect comments expressing negative attitude. Thus, we can draw conclusions over a user behavior and beliefs via the content the user generated within the limits a social medium. We also use an assumption free flat data representation technique in order to decide over the user's attitude. Furthermore, we compare the results of each method and highlight the common behavior manifested by the users. The demonstration is applied on a crawled community of users on YouTube.