A systematic literature review on insider threats

Abstract

Insider threats is the most concerned cybersecurity problem which is poorly addressed by widely used security solutions. Despite the fact that there have been several scientific publications in this area, but from our innovative study classification and structural taxonomy proposals, we argue to provide the more information about insider threats and defense measures used to counter them. While adopting the current grounded theory method for a thorough literature evaluation, our categorization's goal is to organize knowledge in insider threat research. Along with an analysis of major recent studies on detecting insider threats, the major goal of the study is to develop a classification of current types of insiders, levels of access, motivations behind it, insider profiling, security properties, and methods they use to attack. This includes use of machine learning algorithm, behavior analysis, methods of detection and evaluation. Moreover, actual incidents related to insider attacks have also been analyzed.