Volume 15, Number 6

A Systematic Review on Machine Learning Insider Threat Detection Models, Datasets and Evaluation Metrics

  Authors

Everleen Nekesa Wanyonyi^1,2, Silvance Abeka² and Newton Masinde², ¹Murang’a University of Technology, Kenya, ²Jaramogi Oginga Odinga University of Science and Technology, Kenya

  Abstract

Computers are crucial instruments providing a competitive edge to organizations that have adopted them. Their pervasive presence has presented a novel challenge to information security, specifically threats emanating from privileged employees. Various solutions have been tried to address the vice, but no exhaustive solution has been found. Due to their elusive nature, proactive strategies have been proposed of which detection using Machine Learning models has been favoured. The choice of algorithm, datasets and metrics are cornerstones of model performance and hence, need to be addressed. Although multiple studies on ML for insider threat detection have been done, none has provided a comprehensive analysis of algorithms, datasets and metrics for development of Insider Threat Detection models. This study conducts a comprehensive systematic literature review using reputable databases to answer the research questions posed. Search strings, inclusion and exclusion criteria were set for eligibility of articles published in the last decade.

  Keywords

Insider threat, mitigation, Deep Learning, Machine Learning and Confusion Matrix.