Flood Risks of Cyber-Physical Attacks in a Smart Storm Water System

The rise in smart water technologies has introduced new cybersecurity vulnerabilities for water infrastructures. However, the implications of cyber-physical attacks on the systems like urban drainage systems remain underexplored. This research delves into this gap, introducing a method to quantify flood risks in the face of cyber-physical threats. We apply this approach to a smart stormwater system-a real-time controlled network of pond-conduit configurations, fitted with water level detectors and gate regulators. Our focus is on a specific cyber-physical threat: false data injection (FDI). In FDI attacks, adversaries introduce deceptive data that mimics legitimate system noises, evading detection. Our risk assessment incorporates factors like sensor noises and weather prediction uncertainties. Findings reveal that FDIs can amplify flood risks by feeding the control system false data, leading to erroneous outflow directives. Notably, FDI attacks can reshape flood risk dynamics across different storm intensities, accentuating flood risks during less severe but more frequent storms. This study offers valuable insights for strategizing investments in smart stormwater systems, keeping cyber-physical threats in perspective. Furthermore, our risk quantification method can be extended to other water system networks, such as irrigation channels and multi-reservoir systems, aiding in cyber-defense planning. We proposed a mathematical framework for evaluating flood risks of cyber-physical attacks in a smart stormwater systemFalse data injection can maliciously increase inflow and reduce the outflow of a targeted detention pond in a smart stormwater systemAdditional flood risks caused by false data injection are higher with smaller, more frequent storms