Behavioral Biometrics and Machine Learning Models for Insider Threat Prediction : A Conceptual Framework

Insider threats pose a significant challenge to organizational cybersecurity, often eluding traditional security measures due to their origin within trusted entities. The advent of behavioral biometrics capturing unique patterns in user interactions such as keystroke dynamics, mouse movements, and navigation behaviors offers a promising avenue for detecting such threats. When combined with advanced machine learning (ML) techniques, these behavioral indicators can enhance the prediction and prevention of insider threats. This paper presents a conceptual framework that integrates behavioral biometrics with machine learning models to predict insider threats effectively. The framework encompasses the collection of behavioral data, feature extraction, model training, and threat prediction, emphasizing the importance of real-time analysis and adaptability to evolving user behaviors. By leveraging supervised and unsupervised ML algorithms, the framework aims to identify deviations from established behavioral baselines, signaling potential insider threats. The proposed framework addresses challenges such as data privacy concerns, the need for continuous learning to accommodate behavioral changes, and the mitigation of false positives. Through this integration, organizations can proactively detect and respond to insider threats, enhancing their overall security posture. This conceptual framework serves as a foundation for future empirical studies and the development of robust insider threat detection systems.