# Distributed $H_\infty$ Estimation Resilient to Biasing Attacks

## TL;DR

This paper develops a distributed $H_
abla$ estimation method that detects and compensates biasing attacks, enabling each node to produce unbiased, robust estimates while maintaining decentralized gain computation for enhanced security.

## Contribution

It introduces a novel distributed observer design with integrated attack detection and compensation, ensuring unbiased estimates and decentralized gain computation for resilience against biasing attacks.

## Key findings

- Distributed observer achieves unbiased, robust estimates.
- Gains can be computed in a decentralized manner.
- Network vulnerability is reduced through attack compensation.

## Abstract

We consider the distributed $H_\infty$ estimation problem with an 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. The paper proposes a procedure for the derivation of a distributed observer which endows each node with an attack detector which also functions as an attack compensating feedback controller for the main observer. Connecting these controlled observers into a network results in a distributed observer whose nodes produce unbiased robust estimates of the plant. We show that the gains for each controlled observer in the network can be computed in a decentralized fashion, thus reducing vulnerability of the network.