Hybrid Evolutionary-ML Surrogate Models for Cyber-Attack Detection in Water Distribution Networks
Résumé
Water distribution networks (WDNs), as critical infrastructure, face growing cyber-attack risks. While Industry 4.0 initiatives motivate the deployment of edge technologies for monitoring, efficient detection on distributed edge devices requires methods that reduce computational overhead without sacrificing classifier performance. We propose a hybrid evolutionary-machine learning (ML) approach that constructs probabilistic surrogate models of trained ML classifiers using an Estimation-of-Distribution Algorithm (EDA). Our het-EDA algorithm supports the analysis of heterogeneous network communication features, extending r-UMDA for categorical and PBIL-C for continuous data. It evolves class-wise surrogates which optimize the original classifier's output scores. Inference is performed via lightweight log-likelihood evaluation, making the method suitable for resource-constrained edge devices. Experiments on the WDN a Hardware-in-the-Loop dataset show that the surrogates preserve portions of the original decision boundaries, while highlighting limitations due to feature independence assumptions. These results demonstrate the potential of EDA-based probabilistic surrogates for efficient edge ML inference and motivate the development of more expressive EDAs suited for complex industrial network data.
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