Evolutionary tailoring of quantum kernels for cyber-physical anomaly detection: A case study on the SWaT testbed

Quantum computing represents a promising frontier for machine learning, offering new tools to tackle complex problems by leveraging high-dimensional feature spaces and the unique properties of quantum systems. In this context, quantum kernels have emerged as a particularly compelling technique, capable of revealing structures in data that are difficult to detect using classical methods (Schuld and Killoran, 2019 [5]). However, their practical impact in real-world applications remains largely unexplored. This work proposes a hybrid quantum-classical architecture for anomaly detection in industrial cyber-physical systems, using the SWaT testbed as a case study. In complex environments like SWaT, sophisticated cyber-attacks can mimic normal system behavior, making them difficult to identify (Goh et al., 2016 [1]). Quantum kernels may help distinguish such anomalies by enabling more expressive data representations. Experimental results demonstrate the viability of hybrid quantum approaches, providing promising indications for future developments in quantum-supported industrial system security.