Risk Assessment of Stealthy Attacks on Uncertain Control Systems

In this article, we address the problem of risk assessment of stealthy attacks on uncertain control systems. Considering the data injection attacks that aim at maximizing the impact while remaining undetected, we use the recently proposed output-to-output gain to characterize the risk associated with the impact of attacks under a limited system knowledge attacker. The risk is formulated using a well-established risk metric, namely the maximum expected loss. Under this setup, the risk assessment problem corresponds to an untractable infinite nonconvex optimization problem. To address this limitation, we adopt the framework of scenario-based optimization to approximate the infinite nonconvex optimization problem by a sampled nonconvex optimization problem. Then, based on the framework of dissipative system theory and S-procedure, the sampled nonconvex risk assessment problem is formulated as an equivalent convex semidefinite program. Additionally, we derive the necessary and sufficient conditions for the risk to be bounded. Finally, we illustrate the results through numerical simulation of a hydro-turbine power system.