Journal of Engineering Research and Reports

Insider threats present major challenges to energy facilities, particularly where traditional centralized detection systems fall short due to privacy concerns and data variability. This research introduces a federated detection system that integrates biomechanical access controls such as pressure-sensitive floors and biometric interfaces with AI-driven behavioral analytics to enhance threat detection while safeguarding data privacy. A hierarchical federated learning architecture enables collaborative model training across multiple sites without sharing raw data, effectively addressing non-IID data issues. Using multi-modal datasets with 148 biomechanical and 83 cyber features, the system achieved 95% precision, 91% recall, and a 0.97 AUC-ROC, surpassing centralized and local models. Biomechanical authentication reached 98.9% accuracy with low false acceptance and rejection rates, offering consistent behavioral signatures. Combining cyber and physical data through attention-based deep learning boosted overall detection accuracy to over 96%, with detection latency under 150 milliseconds. Privacy was preserved using differential privacy (ε ≤ 1.2), meeting regulatory standards. Validated across diverse energy facilities, the system proves scalable, effective, and privacy-compliant. Recommendations include broader deployment, sensor optimization, and enhancing adversarial robustness.