An Explainable and Privacy-Preserving Federated Learning Model for Threat Detection in Cyber-Physical-Social Systems

Cyber-physical-social systems (CPSS), the technological backbone of infrastructures such as energy, aerospace, transportation, and human-centric applications, are increasingly prone to cyberattacks due to their interconnected nature. Existing threat detection models focus on performance metrics, while effective, often overlook the reliability and privacy of data in CPSS and lack explainability in detecting anomalies. This article introduces an innovative federated learning (FL) model in threat detection for the resilience assessment and enhancement of CPSS. It is not only explainable and privacy-preserving (PP) but also ensures the reliable operation of systems by addressing their inherent vulnerabilities. It addresses current shortcomings, emphasizing the often-neglected accuracy and reliability in literature. In this regard, we reinforce privacy by utilizing differential privacy (DP) and partially homomorphic encryption (PHE), especially the Paillier homomorphic cryptosystem, for secure model update aggregation in the FL environment. The system’s resilience against malicious attempts is heightened by ensuring a reliable aggregation process. To enhance model transparency and facilitate effective responses to threats, we integrate SHapley Additive exPlanations (SHAP) for detailed predictive contribution breakdowns and discern relationships between CPSS’ sensors, cyber components, and social interactions for aberration detection. This improves system reliability by clarifying the contribution of each sensor, device, or social actor, and identifying potential points of failure. The efficacy of our model was rigorously evaluated on three representative CPSS datasets: Secure Water Treatment (SWaT), Gas Pipeline, and UNSW-NB15. It achieved anomaly detection accuracies of 97.46%, 98.85%, and 93.60%, respectively, outperforming several baseline methods. Our model maintains strong privacy guarantees through cryptographic techniques and provides meaningful explainability during anomaly detection, fostering greater trust and reliability in critical CPSS operations.

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