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Detectors on Edge Nodes against False Data Injection on Transmission Lines of Smart Grid

College of Computer, National University of Defense Technology, Changsha 410073, China
Department of Electronic Information and Electrical Engineering, Changsha University, Changsha 410022, China
Author to whom correspondence should be addressed.
Electronics 2018, 7(6), 89;
Received: 23 April 2018 / Revised: 28 May 2018 / Accepted: 31 May 2018 / Published: 4 June 2018
(This article belongs to the Special Issue Cyber-Physical Systems)
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False data injection (FDI) attack is a hot topic in large-scale Cyber-Physical Systems (CPSs), which can cause bad state estimation of controllers. In this paper, we focus on FDI detection on transmission lines of the smart grid. We propose a novel and effective detection framework to identify FDI attacks. Different from the previous methods, there are multi-tier detectors which utilize edge nodes such as the programmable logic controllers (PLCs) instead of the central controller to detect attacks. The proposed framework can decrease the transmission time of data to reduce the latency of decisions because many sensory data need not be transmitted to the central controller for detection. We also develop a detection algorithm which utilizes classifiers based on machine learning to identify FDI. The training process is split from every edge node and is placed on the central node. The detectors are lightweight and are properly adopted in our detection framework. Our simulation experiments show that the proposed detection framework can provide better detection results than the existing detection approaches. View Full-Text
Keywords: cyber-physical system; edge computing; detection; false data injection cyber-physical system; edge computing; detection; false data injection

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Xun, P.; Zhu, P.; Zhang, Z.; Cui, P.; Xiong, Y. Detectors on Edge Nodes against False Data Injection on Transmission Lines of Smart Grid. Electronics 2018, 7, 89.

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