Detection and Mitigation of Biasing Attacks on Distributed Estimation Networks

TL;DR

This paper introduces a method to detect and mitigate biasing attacks in distributed state estimation networks, enhancing their robustness using a vector dissipativity-based detection layer.

Contribution

It proposes a novel attack detection and correction layer for distributed observers within the vector dissipativity framework, improving network resilience.

Findings

The attack detection layer effectively identifies biasing attacks.

The correction mechanism reduces estimation errors caused by attacks.

The approach is validated through a practical example.

Abstract

The paper considers a problem of detecting and mitigating biasing attacks on networks of state observers targeting cooperative state estimation algorithms. The problem is cast within the recently developed framework of distributed estimation utilizing the vector dissipativity approach. The paper shows that a network of distributed observers can be endowed with an additional attack detection layer capable of detecting biasing attacks and correcting their effect on estimates produced by the network. An example is provided to illustrate the performance of the proposed distributed attack detector.