Secure and Trustworthy AI in Cyber-Physical Systems

Scope: Cyber-physical systems (CPS), including smart grids, IoT, vehicular networks, and industrial systems, are increasingly dependent on AI for decision-making and automation. While AI enhances efficiency and resilience, it also introduces new attack surfaces, privacy challenges, and trust concerns. This symposium focuses on methodologies, architectures, and applications for making AI in CPS secure, robust, explainable, and trustworthy.

Topics of Interest

• Intrusion detection and anomaly detection in IoT, CPS, and vehicular networks
• AI-driven security solutions against adversarial attacks
• Privacy-preserving machine learning and federated learning for CPS
• Robust and explainable AI for critical infrastructures
• Secure digital twins and simulation models
• Trust models, resilience, and governance in AI-driven CPS
• Lightweight IDS/IPS for constrained devices

Keywords: AI security, trustworthy AI, CPS, IoT, federated learning, intrusion detection, resilience, explainability.