Predicting the Threat: Investigating Insider Threat Psychological Indicators With Deep Learning

Abstract

The term “insider threat” can take many forms, ranging from an information security risk to the threat of an active shooter. Accordingly, it is beneficial to researchers and practitioners to understand the relationship between psychological factors and the different types of threats an insider may pose to an organization. This research advances this understanding. Specifically, we investigate the three-way relationship between user-generated text, psychological factors espoused in insider threat literature, and risk indicator categories used by the U.S. Government. We employ advancements in machine learning and Natural Language Processing to investigate this relationship. Specifically, we use Bidirectional Encoder Representations from Transformers (BERT) for word embedding and vector space modeling. Our results indicate that there are indeed associations between established risk categories and the psychological factors seen as predictive of malicious insiders. Our exploratory research also reveals that further research is warranted to advance the predictive capability of insider threat modeling.