Proactive Insider Threat Detection Using Facial and Behavioral Biometrics

Insider threats have long posed significant risks to organizational security due to their internal origins and the ethical complexities involved. This paper addresses the critical problem of detecting insider threats by proposing a novel method based on analyzing physiological indicators exhibited by users while interacting with the system. Our approach involves monitoring and recording video footage of users, extracting frames, and analyzing physiological characteristics such as eye movements, facial expressions, and other stress-related indicators. These data points are quantified and assessed to determine the risk level associated with each user. The results demonstrate the effectiveness of our method in identifying potential insider threats with high accuracy. This work’s novelty lies in integrating advanced biometric analysis into insider threat detection, providing a proactive and reliable solution to a pervasive security challenge.