Detection of Biasing Attacks on Distributed Estimation Networks

TL;DR

This paper proposes a method to detect biasing attacks on distributed estimation networks using an $H_ _ ext{infty}$ approach, providing conditions based on linear matrix inequalities to ensure effective detection.

Contribution

It introduces a novel detection framework for biasing attacks in distributed networks with conditions based on linear matrix inequalities.

Findings

Sufficient conditions for attack detection established

Detection method guarantees robustness via $H_ _ ext{infty}$ approach

Framework applicable to various distributed estimation scenarios

Abstract

The paper addresses the problem of detecting attacks on distributed estimator networks that aim to intentionally bias process estimates produced by the network. It provides a sufficient condition, in terms of the feasibility of certain linear matrix inequalities, which guarantees distributed input attack detection using an $H_\infty$ approach.