Malicious Insider Threat Detection Using Sentiment Analysis of Social Media Topics

Malicious insiders often pose a danger to information security systems, which can be a crucial challenge to tackle. Existing technological solutions attempt to identify potential threats via their anomalous system interactions, however, fully fail to suppress the rise in costly data breaches, initiated by trusted users who exploit their authorised access for unauthorised means. Although alternative proposals incorporate a psychosocial angle by utilising correlations between real-world insider cases and their emotional state, personality type or predispositions, they also pose several limitations. In order to mitigate the challenges, this work builds on such profiling methodologies but directly harnesses language as a behavioural indicator, by applying the Natural Language Processing technique of sentiment analysis. It offers a novel approach to lowering the risk of potential insiders and thus taking advantage of the wealth of discourse made public by social media sites to focus on one trait of the narcissist, lack of empathy, and another with a negative correlation with narcissism and compassion. It demonstrates how the careful choice of social media topics can act as a catalyst for language indicating low levels of empathy and compassion, and facilitating the detection of malicious insiders, via their proven tendency towards narcissism.