Modern productive environments increasingly rely on complex cyber-physical systems (CPS) to manage operational processes. While this digitalisation enables higher efficiency and flexibility, it also significantly expands the cyber-attack surface of production systems, exposing CPS to cyber and cyber-physical threats, including side-channel attacks that exploit unintended information leakage from control logic, timing behaviour, or energy consumption patterns, which can compromise safety, availability, and energy efficiency.

This PhD project focuses on the development of advanced cybersecurity solutions for anomaly detection in energy-aware cyber-physical production systems. The research will investigate robust and dependable AI-based techniques capable of detecting both known and previously unseen cyber and cyber-physical attacks, including side-channel-based threats, that affect operational integrity and energy management processes. Particular attention will be given to the fusion of multi-modal data sources, including industrial network traffic, control signals, process variables, and energy-related measurements characteristic of CPS, which may reveal anomalous or covert behaviours.

The project will explore adaptive and real-time anomaly detection methods for CPS, as well as explainable AI (XAI) techniques to support human operators in understanding security incidents and assessing their impact on energy usage, system reliability, and production continuity.