Empirical Detection Techniques of Insider Threat Incidents

Vital organizations have faced increasing challenges of how to defend against insider threats that may cause a severe damage to their assets. The nature of insider threats is more challenging than external threats, as insiders have a privileged access to sensitive assets of an organization. In fact, there are several studies that reviewed the insider threat detection approaches from taxonomical and theoretical perspectives. However, the protection against insider threat incidents requires empirical defense solutions. Hence, our study uniquely focuses on empirical detection approaches that are validated with empirical results. We propose a 10-question model that highlights different prospective of empirical detection approaches. Significant factors are also proposed to reveal the extent to which the detection approaches are effective against insider threat incidents (e.g., feature domains, protection coverage, classification techniques, simulated scenarios, performance and accuracy metrics, etc.). The objective of this paper is to enhance researchers' efforts in the domain of insider attack by systemizing the detection techniques in comparable manner. It also highlights the challenges and gaps for further research to institute more effective solutions that can predict, detect, and prevent emerging attack incidents. Some recommendations for future research directions are also presented.