An insider threat prediction model

Miltiadis Kandias, Alexios Mylonas, Nikos Virvilis, Marianthi Theoharidou, Dimitris Gritzalis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

91 Citations (Scopus)


Information systems face several security threats, some of which originate by insiders. This paper presents a novel, interdisciplinary insider threat prediction model. It combines approaches, techniques, and tools from computer science and psychology. It utilizes real time monitoring, capturing the user's technological trait in an information system and analyzing it for misbehavior. In parallel, the model is using data from psychometric tests, so as to assess for each user the predisposition to malicious acts and the stress level, which is an enabler for the user to overcome his moral inhibitions, under the condition that the collection of such data complies with the legal framework. The model combines the above mentioned information, categorizes users, and identifies those that require additional monitoring, as they can potentially be dangerous for the information system and the organization.

Original languageEnglish
Title of host publicationTrust, Privacy and Security in Digital Business - 7th International Conference, TrustBus 2010, Proceedings
Number of pages12
Publication statusPublished - 2010
Event7th International Conference on Trust, Privacy and Security in Digital Business, TrustBus 2010 - Bilbao, Spain
Duration: 30 Aug 201031 Aug 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6264 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference7th International Conference on Trust, Privacy and Security in Digital Business, TrustBus 2010


  • Information Security
  • Insider Threat
  • Prediction
  • Taxonomy


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