Consensus based Detection in the Presence of Data Falsification Attacks

TL;DR

This paper addresses the challenge of detecting data falsification attacks in distributed networks using consensus algorithms, proposing robust and adaptive methods to improve detection performance under Byzantine attacks.

Contribution

It introduces a robust distributed weighted consensus algorithm and learning-based techniques for adaptive detection in the presence of Byzantine data falsification.

Findings

The proposed consensus algorithm is resilient to Byzantine attacks.

Distributed detection performance is improved with the new algorithms.

Adaptive techniques enable effective detection even with unknown data distribution parameters.

Abstract

This paper considers the problem of detection in distributed networks in the presence of data falsification (Byzantine) attacks. Detection approaches considered in the paper are based on fully distributed consensus algorithms, where all of the nodes exchange information only with their neighbors in the absence of a fusion center. In such networks, we characterize the negative effect of Byzantines on the steady-state and transient detection performance of the conventional consensus based detection algorithms. To address this issue, we study the problem from the network designer's perspective. More specifically, we first propose a distributed weighted average consensus algorithm that is robust to Byzantine attacks. We show that, under reasonable assumptions, the global test statistic for detection can be computed locally at each node using our proposed consensus algorithm. We exploit the statistical distribution of the nodes' data to devise techniques for mitigating the influence of data falsifying Byzantines on the distributed detection system. Since some parameters of the statistical distribution of the nodes' data might not be known a priori, we propose learning based techniques to enable an adaptive design of the local fusion or update rules.