Localizing of Anomalies in Critical Infrastructure using Model-Based Drift Explanations

Facing climate change, the already limited availability of drinking water will decrease in the future, rendering drinking water an increasingly scarce resource. Considerable amounts of it are lost through leakages in water transportation and distribution networks. Thus, anomaly detection and localization, in particular for leakages, are crucial but challenging tasks due to the complex interactions and changing demands in water distribution networks. In this work, we conceptually analyze the effects of anomalies on the dynamics of critical infrastructure systems by modeling them with Bayesian networks. We then discuss how the problem is connected to and can be considered through the lens of concept drift. This analysis yields our proposal to leverage model-based drift explanations as a tool for localizing anomalies given limited information about the network. The methodology is experimentally evaluated using realistic benchmark scenarios. To showcase that our methodology applies to critical infrastructure more generally, in addition to considering leakages and sensor faults in water systems, we investigate the suitability of the derived technique to localize sensor faults in power systems.