Detection of Biasing Attacks on Distributed Estimation Networks

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.

The paper is to appear in Proceedings of the 55th IEEE Conference on Decision and Control, Las Vegas, December 2016