Enhancing Intrusion Detection in CPS and IIoT with Lightweight Explainable AI Models
Amanda Ericson, Kyi Thar, Stefan Forsström · 2025
Integrating cyber-physical systems and the Internet of Things into industrial operations has significantly improved automation, efficiency, and data-driven decision making. However, these advances have also made industrial environments more vulnerable to cybersecurity risks. Our previous work explored lightweight deep learning models for real-time intrusion detection systems on edge devices, yet these models often operate as black boxes, limiting their trustworthiness. This issue is especially critical in the European Union, where the AI Act mandates transparency, accountability, and human oversight for AI solutions to be interpretable. In this paper, we integrate explainable AI solutions into lightweight real-time intrusion detection systems on edge devices to enhance the transparency and interpretability of black-box models. The study demonstrates that integrating SHapley Additive exPlanations significantly enhances the interpretability of intrusion detection systems, providing more transparent insights into model decisionmaking processes while maintaining accuracy and computational efficiency. This work contributes to the development of more secure and trustworthy industrial ecosystems by improving the effectiveness and reliability of intrusion detection.