Resilient Distributed $H_\infty$ Estimation via Dynamic Rejection of Biasing Attacks

TL;DR

This paper introduces a resilient distributed $H_ abla$ estimation method that detects and rejects biasing attacks, enabling the network to maintain accurate state estimates even when some nodes are compromised.

Contribution

A novel distributed estimation algorithm that incorporates attack detection filters, allowing real-time, decentralized resilience against biasing attacks in networked systems.

Findings

The proposed method effectively detects biasing attacks in real time.

The network maintains unbiased state estimates despite node compromises.

The algorithm is computationally feasible for real-time decentralized implementation.

Abstract

We consider the distributed $H_\infty$ estimation problem with additional requirement of resilience to biasing attacks. An attack scenario is considered where an adversary misappropriates some of the observer nodes and injects biasing signals into observer dynamics. Using a dynamic modelling of biasing attack inputs, a novel distributed state estimation algorithm is proposed which involves feedback from a network of attack detection filters. We show that each observer in the network can be computed in real time and in a decentralized fashion. When these controlled observers are interconnected to form a network, they are shown to cooperatively produce an unbiased estimate the plant, despite some of the nodes are compromised.