Resilience Assessment of Water Quality Sensor Designs under Cyber-Physical Attacks

Abstract

Water distribution networks (WDNs) are critical infrastructure for the welfare of society. Due to their spatial extent and difficulties in deployment of security measures, they are vulnerable to threat scenarios that include the rising concern of cyber-physical attacks. To protect WDNs against different kinds of water contamination, it is customary to deploy water quality (WQ) monitoring sensors. Cyber-attacks on the monitoring system that employs WQ sensors combined with deliberate contamination events via backflow attacks can lead to severe disruptions to water delivery or even potentially fatal consequences for consumers. As such, the water sector is in immediate need of tools and methodologies that can support cyber-physical quality attack simulation and vulnerability assessment of the WQ monitoring system under such attacks. In this study we demonstrate a novel methodology to assess the resilience of placement schemes generated with the Threat Ensemble Vulnerability Assessment and Sensor Placement Optimization Tool (TEVA-SPOT) and evaluated under cyber-physical attacks simulated using the stress-testing platform RISKNOUGHT, using multidimensional metrics and resilience profile graphs. The results of this study show that some sensor designs are inherently more resilient than others, and this trait can be exploited in risk management practices.

Stress-Testing Framework for Urban Water Systems: A Source to Tap Approach for Stochastic Resilience Assessment

“…However, it must be noted that this resilience assessment methodology is also suitable for other performance metrics. For example, in Nikolopoulos et al [22], the detection ratio of cyber-physical attacks in the context of cyber-physical water systems was used as a performance metric for resilience assessment. In the resilience profile graph (see Figure 2), the area under the (reliability) curve is defined as resilience.…”

“…Uncertainty related to both short-and longer-term stresses needs to be considered using stochastically generated inputs and parameters (e.g., synthetic supply and demand patterns), as well as scenario-based approaches [21]. In a Monte Carlo context, these alternative scenarios can be used as inputs to stress-test alternative system designs under a variety of future conditions, including specific threats, such as cyber-physical attacks (see [9,[22][23][24]). Stochastic computational methods can also be used to generate valid topologies of UWS subsystems and test their designs (such as, for example the approach adopted by Zhang et al [25] assessing combined sewer systems' resilience).…”

Smart Water Infrastructures Laboratory: Reconfigurable Test-Beds for Research in Water Infrastructures Management

“…Although the digitalisation of these critical infrastructures with AI, wireless networks and IoT sensors considerably improve the monitoring and management of the infrastructure, it can make them more vulnerable to malicious attacks including, among others, cyber-attacks [25]. Some studies have addressed the security in water systems for improving the next generation of cyber-physical systems [26].…”

An Adaptive Protection System for Sensor Networks Based on Analysis of Neighboring Nodes

“…Often the sensory system is located outside a controlled area, in natural conditions, or outdoors. The nodes of the system may be mobile, the architecture of the system can be periodically updated, and new nodes may be added [ 9 ]. Cryptographic and authentication methods cannot always ensure the complete security of such a system.…”