Cooperative Distributed State Estimation: Resilient Topologies against Smart Spoofers

TL;DR

This paper investigates the vulnerability of asynchronous distributed state estimation networks to smart spoofing attacks and proposes topological conditions to ensure resilient convergence against such adversaries.

Contribution

It introduces a novel analysis of smart spoofers in asynchronous networks and establishes robust topological conditions for resilient state estimation.

Findings

Asynchronous networks are vulnerable to smart spoofing attacks.

Distributive filtering of extreme values enhances resilience.

Strong robustness conditions guarantee convergence despite adversaries.

Abstract

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.