Localizing Anomalies in Critical Infrastructure using Model-Based Drift Explanations
This work addresses the problem of reducing resource loss, like water leakages, for infrastructure managers, but it appears incremental as it applies existing methods to new domains.
The paper tackles anomaly detection and localization in critical infrastructure, such as water and power systems, by modeling networks with Bayesian networks and using model-based explanations of concept drift, achieving experimental evaluation on realistic benchmark scenarios.
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 analyze the effects of anomalies on the dynamics of critical infrastructure systems by modeling the networks employing Bayesian networks. We then discuss how the problem is connected to and can be considered through the lens of concept drift. In particular, we argue that model-based explanations of concept drift are a promising 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 showcase the suitability of the derived technique to localize sensor faults in power systems.