Distributed Detection and Mitigation of Biasing Attacks over Multi-Agent Networks

TL;DR

This paper introduces a distributed method for detecting and mitigating biasing attacks in multi-agent networks, ensuring system observability and robustness through probabilistic residual thresholds and graph-theoretic mitigation strategies.

Contribution

It presents a novel distributed attack detection and mitigation framework that handles non-local observability and uses probabilistic thresholds for residual-based attack detection.

Findings

Distributed estimation is unbiased with bounded mean-square deviation.

Residual-based attack detection with probabilistic thresholds is effective.

Graph-theoretic mitigation restores system observability after attack detection.

Abstract

This paper proposes a distributed attack detection and mitigation technique based on distributed estimation over a multi-agent network, where the agents take partial system measurements susceptible to (possible) biasing attacks. In particular, we assume that the system is not locally observable via the measurements in the direct neighborhood of any agent. First, for performance analysis in the attack-free case, we show that the proposed distributed estimation is unbiased with bounded mean-square deviation in steady-state. Then, we propose a residual-based strategy to locally detect possible attacks at agents. In contrast to the deterministic thresholds in the literature assuming an upper bound on the noise support, we define the thresholds on the residuals in a probabilistic sense. After detecting and isolating the attacked agent, a system-digraph-based mitigation strategy is proposed to replace the attacked measurement with a new observationally-equivalent one to recover potential observability loss. We adopt a graph-theoretic method to classify the agents based on their measurements, to distinguish between the agents recovering the system rank-deficiency and the ones recovering output-connectivity of the system digraph. The attack detection/mitigation strategy is specifically described for each type, which is of polynomial-order complexity for large-scale applications. Illustrative simulations support our theoretical results.