Coordinated Cyber-Attack Detection Model of Cyber-Physical Power System Based on the Operating State Data Link

Frontiers in Energy Research

Yang Li¹  Lei Wang²  Zhaoyang Qu²  Zhenming Zhang²  Xiaoyong Bo²  Yunchang Dong²  Pengcheng Xu³ 

[1] School of Electrical Engineering, Northeast Electric Power University, Jilin, China;School of Electrical Engineering, Northeast Electric Power University, Jilin, China;Jilin Engineering Technology Research Center of Intelligent Electric Power Big Data Processing, Jilin, China;Siping Power Supply Company of State Grid Jilin Electric Power Company Limited, Siping, China;

关键词: cyber-physical power system;    coordinated cyber-attack;    cluster analysis;    oversampling;    gradient boosting decision tree;   

DOI  :  10.3389/fenrg.2021.666130

来源: Frontiers

【 摘 要 】

Existing coordinated cyber-attack detection methods have low detection accuracy and efficiency and poor generalization ability due to difficulties dealing with unbalanced attack data samples, high data dimensionality, and noisy data sets. This paper proposes a model for cyber and physical data fusion using a data link for detecting attacks on a Cyber–Physical Power System (CPPS). The two-step principal component analysis (PCA) is used for classifying the system’s operating status. An adaptive synthetic sampling algorithm is used to reduce the imbalance in the categories’ samples. The loss function is improved according to the feature intensity difference of the attack event, and an integrated classifier is established using a classification algorithm based on the cost-sensitive gradient boosting decision tree (CS-GBDT). The simulation results show that the proposed method provides higher accuracy, recall, and F-Score than comparable algorithms.

【 授权许可】

CC BY