Robust Optimal Network Topology Switching for Zero Dynamics Attacks

TL;DR

This paper introduces a robust and optimal topology switching framework to detect and mitigate zero dynamics attacks in networked control systems, enhancing resilience against stealthy cyber-physical threats.

Contribution

It reformulates the ZDA mitigation problem into a convex rank-constrained optimization, enabling effective detection and mitigation through topology switching.

Findings

Successfully applied to networked double integrators under ZDAs.

Demonstrates improved detection and mitigation performance.

Provides a convex optimization approach for resilient control topology design.

Abstract

The intrinsic, sampling, and enforced zero dynamics attacks (ZDAs) are among the most detrimental stealthy attacks in robotics, aerospace, and cyber-physical systems. They exploit internal dynamics, discretization, redundancy/asynchronous actuation and sensing, to construct disruptive attacks that are completely stealthy in the measurement. They work even when the systems are both controllable and observable. This paper presents a novel framework to robustly and optimally detect and mitigate ZDAs for networked linear control systems. We utilize controllability, observability, robustness, and sensitivity metrics written explicitly in terms of the system topology, thereby proposing a robust and optimal switching topology formulation for resilient ZDA detection and mitigation. Our main contribution is the reformulation of this problem into an equivalent rank-constrained optimization problem (i.e., optimization with a convex objective function subject to convex constraints and rank constraints), which can be solved using convex rank minimization approaches. The effectiveness of our method is demonstrated using networked double integrators subject to ZDAs.