Water Distribution

Oral

395191 - Sensitivity Analysis of an Optimization Approach to Identify Cyber Attacks on Water Distribution Networks

Wednesday, June 6
8:30 AM - 10:00 AM
Location: Lakeshore C
Co-Authors: Nikolaos Gatsis, San Antonio – University of Texas at San Antonio; Marcio Giacomoni, San Antonio – University of Texas at San Antonio

Cyber-physical systems are becoming key instruments in the operation and monitoring of critical infrastructure, such as water distribution networks (WDNs). The integration of physical interconnected elements with sensors and controllers allow the efficient distribution of potable water. Despite the economic and social impact of such technology, cyber-physical systems can be vulnerable to cyber-attacks that can compromise the well-being of consumers. Moreover, a vast number of security incidents have been recently reported on water facilities due to cyber-attacks. New detection and mitigation technologies are, therefore, highly needed to better identify and avoid incidents on WDNs. The present study tests and develops a sensitivity analysis of an optimization approach that unveils the low-dimensionality components in sensory data measurements and anomalies produced by cyber-attacks in WDNs. The proposed methodology was tested on a hypothetical real-world medium-sized WDN with simulated cyber-attacks. The WDN measurements were evaluated by using convex optimization as the chief computational instrument. The results of the optimization detection-approach show that a variety of cyber-attacks were identified with relatively small computation time. However, in some cases, the algorithm found false positive attacks, which requires improvement in the calibration of the optimization parameters. The optimization detection-approach is expected to assist water managers and operators to make WDNs less vulnerable to cyber-attacks.

Herta P. Montoya

University of Texas at San Antonio

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395191 - Sensitivity Analysis of an Optimization Approach to Identify Cyber Attacks on Water Distribution Networks



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