What is it about?
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).
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Why is it important?
The simulation results show that the proposed method provides higher accuracy, recall, and F-Score than comparable algorithms.
Perspectives
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).
Professor/PhD Supervisor/SMIEEE Yang Li
Northeast Electric Power University
Read the Original
This page is a summary of: Coordinated Cyber-Attack Detection Model of Cyber-Physical Power System Based on the Operating State Data Link, Frontiers in Energy Research, April 2021, Frontiers,
DOI: 10.3389/fenrg.2021.666130.
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