Research Article
Performance Analysis of Consensus-Based Distributed System Under False Data Injection Attacks
@INPROCEEDINGS{10.1007/978-3-030-41114-5_36, author={Xiaoyan Zheng and Lei Xie and Huifang Chen and Chao Song}, title={Performance Analysis of Consensus-Based Distributed System Under False Data Injection Attacks}, proceedings={Communications and Networking. 14th EAI International Conference, ChinaCom 2019, Shanghai, China, November 29 -- December 1, 2019, Proceedings, Part I}, proceedings_a={CHINACOM}, year={2020}, month={2}, keywords={Consensus-based distributed system False data injection attack (FDIA) Performance analysis Convergence}, doi={10.1007/978-3-030-41114-5_36} }- Xiaoyan Zheng
Lei Xie
Huifang Chen
Chao Song
Year: 2020
Performance Analysis of Consensus-Based Distributed System Under False Data Injection Attacks
CHINACOM
Springer
DOI: 10.1007/978-3-030-41114-5_36
Abstract
This paper investigates the security problem of consensus-based distributed system under false data injection attacks (FDIAs). Since the injected false data will spread to the whole network through data exchange between neighbor nodes, and result in continuing effect on the system performance, it is significant to study the impact of the attack. In this paper, we consider two attack models according to the property of the injection data, the deterministic attack and the stochastic attack. Then, the necessary and sufficient condition for the convergence of distributed system under the attack are derived, and the attack feature making the system unable to converge is provided. Moreover, the convergence result under resource-limited attack is deviated. On the other hand, the statistical properties of the convergence performance under zero-mean and non-zero-mean stochastic attacks are analyzed, respectively. Simulation results illustrate the effects caused by FDIAs on the convergence performance of distributed system.
