Distributed Estimation over Directed Graphs Resilient to Sensor Spoofing

TL;DR

This paper introduces the REWB algorithm for resilient distributed estimation over directed networks, effectively countering sensor spoofing attacks when less than half of the agents are compromised.

Contribution

The paper proposes a novel REWB algorithm that ensures resilient estimation in directed graphs under sensor spoofing attacks, requiring less than half of the agents to be under attack.

Findings

REWB guarantees accurate estimation under attack conditions.

The algorithm performs well in simulations with various attack scenarios.

Resilience is achieved with less than half the agents compromised.

Abstract

This paper addresses the problem of distributed estimation of an unknown dynamic parameter by a multi-agent system over a directed communication network in the presence of an adversarial attack on the agents' sensors. The mode of attack of the adversaries is to corrupt the sensor measurements of some of the agents, while the communication and information processing capabilities of those agents remain unaffected. To ensure that all the agents, both normal as well as those under attack, are able to correctly estimate the parameter value, the Resilient Estimation through Weight Balancing (REWB) algorithm is introduced. The only condition required for the REWB algorithm to guarantee resilient estimation is that at any given point in time, less than half of the total number of agents are under attack. The paper discusses the development of the REWB algorithm using the concepts of weight balancing of directed graphs, and the consensus+innovations approach for linear estimation. Numerical simulations are presented to illustrate the performance of our algorithm over directed graphs under different conditions of adversarial attacks.