Reinforcement Learning-Empowered Graph Convolutional Network Framework for Data Integrity Attack Detection in Cyber-Physical Systems

Edeh Vincent; Mehdi Korki; Mehdi Seyedmahmoudian; Alex Stojcevski; Saad Mekhilef

doi:10.17775/CSEEJPES.2023.01250

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Reinforcement Learning-Empowered Graph Convolutional Network Framework for Data Integrity Attack Detection in Cyber-Physical Systems

Special Section Papers | 更新时间：2026-10-15

- Reinforcement Learning-Empowered Graph Convolutional Network Framework for Data Integrity Attack Detection in Cyber-Physical Systems
- Reinforcement Learning-Empowered Graph Convolutional Network Framework for Data Integrity Attack Detection in Cyber-Physical Systems
- 中国电机工程学会电力与能源系统学报(英文) 2024年10卷第2期页码：797-806
- 作者机构：
  
  School of Science, Computing, and Engineering Technologies, Swinburne University of Technology,Melbourne,Australia
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2023.01250
  中图分类号：
- 纸质出版：2024-03-01
- 稿件说明：
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Edeh Vincent, Mehdi Korki, Mehdi Seyedmahmoudian, 等. Reinforcement Learning-Empowered Graph Convolutional Network Framework for Data Integrity Attack Detection in Cyber-Physical Systems[J]. 中国电机工程学会电力与能源系统学报(英文), 2024,10(2):797-806.

Edeh Vincent, Mehdi Korki, Mehdi Seyedmahmoudian, et al. Reinforcement Learning-Empowered Graph Convolutional Network Framework for Data Integrity Attack Detection in Cyber-Physical Systems[J]. CSEE Journal of Power and Energy Systems, 2024, 10(2): 797-806.
Edeh Vincent, Mehdi Korki, Mehdi Seyedmahmoudian, 等. Reinforcement Learning-Empowered Graph Convolutional Network Framework for Data Integrity Attack Detection in Cyber-Physical Systems[J]. 中国电机工程学会电力与能源系统学报(英文), 2024,10(2):797-806. DOI： 10.17775/CSEEJPES.2023.01250.

Edeh Vincent, Mehdi Korki, Mehdi Seyedmahmoudian, et al. Reinforcement Learning-Empowered Graph Convolutional Network Framework for Data Integrity Attack Detection in Cyber-Physical Systems[J]. CSEE Journal of Power and Energy Systems, 2024, 10(2): 797-806. DOI： 10.17775/CSEEJPES.2023.01250.

摘要

Abstract

The massive integration of communication and information technology with the large-scale power grid has enhanced the efficiency

safety

and economical operation of cyber-physical systems. However

the open and diversified communication environment of the smart grid is exposed to cyber-attacks. Data integrity attacks that can bypass conventional security techniques have been considered critical threats to the operation of the grid. Current detection techniques cannot learn the dynamic and heterogeneous characteristics of the smart grid and are unable to deal with non-euclidean data types. To address the issue

we propose a novel Deep-Q-Network scheme empowered with a graph convolutional network (GCN) framework to detect data integrity attacks in cyber-physical systems. The simulation results show that the proposed framework is scalable and achieves higher detection accuracy

unlike other benchmark techniques.

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