Machine learning and user interface for cyber risk management of water infrastructure

With the continuous modernization of water plants, the risk of cyberattacks on them potentially endangers public health and the economic efficiency of water treatment and distribution. This article signifies the importance of developing improved techniques to support cyber risk management for critical water infrastructure, given an evolving threat environment. In particular, we propose a method that uniquely combines machine learning, the theory of belief functions, operational performance metrics, and dynamic visualization to provide the required granularity for attack inference, localization, and impact estimation. We illustrate how the focus on visual domain-aware anomaly exploration leads to performance improvement, more precise anomaly localization, and effective risk prioritization. Proposed elements of the method can be used independently, supporting the exploration of various anomaly detection methods. It thus can facilitate the effective management of operational risk by providing rich context information and bridging the interpretation gap.