Micromovement Behavior as an Intention Detection Measurement for Preventing Insider Threats

Abstract

Behavioral measurements have provided insightful information in many research areas, including insider threats; however, none of the previous research investigated the use of head micromovement patterns while presenting visual stimuli that represent possible malicious intentions to detect insiders’ intentions; hence, to detect insider threats. In this paper, we hypothesize that involuntary head micromovement patterns toward visual stimuli allow for detecting intentions of access. Because detecting intentions does not state the probability of an intention to be executed, we hypothesize that the motivation toward an intention is inversely correlated with the number of micromovements. This paper also investigates the potential of substituting the physiological measurements of the intent-based access control (IBAC) model, which is a risk-based access control model that assesses risk based on involuntary physiological signals, electroencephalogram data, with the proposed behavioral measurements and compares the results. We assessed the IBAC model with the proposed behavioral measurement using experiments on 30 participants and it suggests the potential of the proposed behavioral measurement with 100% intention detection accuracy, similar to the physiological measurement results; however, the behavioral measurement showed 100% correctly accepted, but 70% correctly rejected users when compared with the physiological measurement, which showed 100% correctly accepted and rejected users. Further, the behavioral measurement showed 80% user acceptance when compared with the physiological measurement, which showed only 10% user acceptance.