Cooperative Distributed State Estimation: Resilient Topologies against Smart Spoofers
This addresses security in distributed estimation for networks like sensor systems, but it is incremental as it builds on existing robustness concepts.
The paper tackles the problem of cooperative state estimation in networks vulnerable to smart spoofing attacks, where adversaries impersonate nodes and disrupt estimation, and proposes topological conditions based on strong robustness to ensure convergence, with results verified through two simulation scenarios.
A network of observers is considered, where through asynchronous (with bounded delay) communications, they cooperatively estimate the states of a Linear Time-Invariant (LTI) system. In such a setting, a new type of adversary might affect the observation process by impersonating the identity of the regular node, which is a violation of communication authenticity. These adversaries also inherit the capabilities of Byzantine nodes, making them more powerful threats called smart spoofers. We show how asynchronous networks are vulnerable to smart spoofing attack. In the estimation scheme considered in this paper, information flows from the sets of source nodes, which can detect a portion of the state variables each, to the other follower nodes. The regular nodes, to avoid being misguided by the threats, distributively filter the extreme values received from the nodes in their neighborhood. Topological conditions based on strong robustness are proposed to guarantee the convergence. Two simulation scenarios are provided to verify the results.