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

Abstract

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.

Ensemble Learning-Enabled Security Anomaly Identification for IoT Cyber–Physical Power Systems

“…In [17,18], two effective cyber-attack schemes for remote control units (RTUs) were proposed by Lallie et al and Wang et al, respectively. Meanwhile, a method was proposed based on an improved attack graph to evaluate the hazard of crossspace cascading faults in cyber-physical power systems [19,20]. Zhu et al [21] underlined the comparison requirement in a broader range of settings, which provided promising suggestions for further work.…”

Online Cyber-Attack Detection in the Industrial Control System: A Deep Reinforcement Learning Approach

“…And they use physical knowledge to intelligently derive signi cant features from a large number of noisy physical measurements [10]. Machine learning methods were used to identify the difference between normal activities and attack activities [11]. Wang et al [12] proposed a network attack identification algorithm based on temporal causality Bayesian network for the extraction of cooperative attack modes of physical systems in power networks.…”