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Modern Water Distribution Systems (WDSs) are often controlled by Supervisory Control and Data Acquisition (SCADA) systems and Programmable Logic Controllers (PLCs) which manage their operation and maintain a reliable water supply. As such, and with the cyber layer becoming a central component of WDS operations, these systems are at a greater risk of being subjected to cyberattacks. This paper offers a model-based methodology based on a detailed hydraulic understanding of WDSs combined with an anomaly detection algorithm for the identification of complex cyberattacks that cannot be fully identified by hydraulically based rules alone. The results show that the proposed algorithm is capable of achieving the best-known performance when tested on the data published in the BATtle of the Attack Detection ALgorithms (BATADAL) competition ( http://www.batadal.net ). [ABSTRACT FROM AUTHOR]

CANARY, developed by the U.S. Environmental Protection Agency (USEPA), is a freeware designed for contamination events detection in water distribution systems. CANARY has several imbedded statistical methods for analyzing water quality data to detect contamination events. The imbedded methods require calibration of their parameters to achieve good performance. However, the multiobjective nature of the problem creates a conflict between high sensitivity which results in good detection but with many false alerts, and low sensitivity which results in a small number of false alarms but with poor detection. In this work, a MOGA-CANARY add-in for CANARY autocalibration is introduced. This tool could be used by CANARY users to find the optimal parameter configuration that fits their system. MOGA-CANARY gives the users the whole set of Pareto optimal system configurations based on their defined objectives. The results of MOGA-CANARY are compared with existing manual calibration methods proposed in CANARY documentation. [ABSTRACT FROM AUTHOR]

This study deals with the integration of contamination simulations and a spatial event detection model. The simulation of contaminant intrusion includes detailed chemical-specific reactions within a multi-species water quality model. This set-up generates a scenario of contaminant distribution and produces a continuous multiple sensor stations database. Three organophosphates pesticides, Chlorpyrifos, Malathion, and Parathion, are modeled as possible contaminants. The event detection model comprises both local and spatial data analysis. The local model applies a previously developed single-sensor event detection model with a higher alert threshold that reduces false alarm rates. The spatial model considers upstream sensor datasets which are examined for their uniqueness and mutual resemblance in a sliding time window. The model utilizes outlier detection, data analyses, and network hydraulics for the detection of suspicious spatial trends. The proposed algorithm is capable of detecting events with low contamination signatures and spatial influence. Two case studies are explored and compared to the single sensor model. The proposed methodology resulted in a lower number of false alarms compared to the previous single sensor event detection modeling approach. [ABSTRACT FROM AUTHOR]