Attack Identification for Nonlinear Systems Based on Sparse Optimization

Abstract

Adversarial attacks on controllers of dynamic systems have become a serious threat to many real-world systems, making methods for fast identification of attacks an indispensable part of autonomous systems. With the increasing use of model-based controllers, it is valid to exploit model knowledge also for attack identification as long as privacy of individual components is maintained. A scalable, model-based method to reveal generic attacks was introduced in our previous work and is further investigated here. It is designed for coupled systems with nonlinear dynamics and monitors certain coupling states. Based on the exchange of local sensitivity information, it approximates the propagation of an attack through the network and solves a sparse optimization problem to identify the attack. We provide a thorough derivation of the approach and analyze the involved approximation errors to prove rigorous guarantees for successful identification. In an extensive numerical case study with the IEEE 30 bus power system, we prove that not only the guarantees apply for a nonlinear, practically relevant example, but the method also identifies attacked buses with very high success rates.