Title: "Identifying the Psychological Indicators and Mitigation Techniques To Reduce Insider Threats "

Page(s): 26-30
Authors: T. Kirthiga Devi,Prof. S. Rajendran


Abstract— Threats from the insider are difficult to identify and resolve because of true nature of the action. In protecting the integrity of our systems and data against insider threats is to monitor network access, the database for unusual activity, especially important and critical. The proposed framework will focus on monitoring the activities of internal users and predicting their next activities to provide real time or near real time alerts on violation or other suspicious activity. It will provide a detailed statistics on defining and capturing the relationship between elements for instance, how the insider’s psychological state will impact with their motivation on attack using statistical model. Statistics of past attacks, for how often individuals have exhibited a particular set of attributes and resultant outcomes, is used to determine whether an insider would involve in malicious activity and the attack patterns. The framework may then allow experts to infer the risk associated with observing a series of states within the system.

Keywords — Psychological Indictors, Bayesian Network, Model, Parameters.