Important-Data-Based DoS Attack Strategy and $H$$_{∞}$ State Estimator Design for a Class of Nonlinear Systems

This article investigates the security of a class of nonlinear systems subject to denial-of-service (DoS) attacks from an adversarial, data-aware perspective. An important-data-based (IDB) DoS attack strategy, inspired by event-triggered logic from the perspective of attackers, is skillfully constructed to increase the destruction of the attack. Unlike most existing DoS attack models that indiscriminately disrupt packet transmissions without considering packet content, the proposed IDB DOS attack strategy can identify the importance degree of the packets and focus on attacking the most important ones. The other goal of this article is to construct an$H_{\infty }$state estimator against the IDB DoS attack, which can achieve a particular system performance in the presence of attacks. By using the Lyapunov functional method, a sufficient condition is successfully obtained to ensure the asymptotical stability of the augmented system and the$H_{\infty }$interference suppression performance. Simulation examples indicate that: 1) the constructed IDB DoS attack strategy will result in a worse system degradation and 2) the designed$H_{\infty }$state estimator can effectively mitigate the impact of the potential IDB DoS attack.