Open Access
A FEDERATED MULTI-MODAL SYSTEM FOR INSIDER THREAT DETECTION IN ENERGY INFRASTRUCTURE USING BIOMETRIC AND CYBER DATA
4
School of Computer Science, National Institute of Technology Tiruchirappalli, India
4
Cyber-Physical Systems Laboratory, University of Texas at Austin, USA
Abstract
Insider threats pose significant risks to the operational continuity and security of critical energy infrastructure. This paper presents a federated multi-modal system that integrates biometric and cyber data to detect insider threats with high accuracy while preserving data privacy. The proposed architecture combines facial recognition, keystroke dynamics, and network activity logs using a federated learning framework, enabling decentralized model training across multiple nodes. This approach reduces data exposure risks and supports compliance with privacy regulations. Experimental evaluations on synthetic and real-world datasets demonstrate the system’s effectiveness in identifying anomalous user behavior patterns, outperforming centralized baselines in both detection rate and resilience. The study offers a scalable, privacy-aware solution for securing energy systems against internal cyber-physical threats.
Keywords
Insider threat detection,
federated learning,
multi-modal system,
biometric authentication
References
Glasser, J., & Lindauer, B. (2013). Bridging the gap: A pragmatic approach to generating insider threat data. In 2013 IEEE Security and Privacy Workshops (pp. 98–104).
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Das, S., & Borisov, N. (2019). Privacy-preserving image feature extraction for face recognition. In Proceedings on Privacy Enhancing Technologies, (1), 203–219.
Salem, M. B., Hershkop, S., & Stolfo, S. J. (2008). A survey of insider attack detection research. In Insider Attack and Cyber Security (pp. 69–90). Springer.
Google AI Blog. (2017). Federated Learning: Collaborative Machine Learning without Centralized Training Data. Retrieved from https://ai.googleblog.com/2017/04/federated-learning-collaborative.html
Liu, W., Yang, L., & Lu, S. (2020). Federated learning for privacy-preserving network security systems. IEEE Network, 34(6), 20–25.
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