Network Attack Detection Method of the Cyber-Physical Power System Based on Ensemble Learning

With the rapid development of power grid informatization, the power system has evolved into a multi-dimensional heterogeneous complex system with high cyber-physical integration, denoting the Cyber-Physical Power System (CPPS). Network attack, in addition to faults, becomes an important factor restricting the stable operation of the power system. Under the influence of network attacks, to improve the operational stability of CPPSs, this paper proposes a CPPS network attack detection method based on ensemble learning. First, to solve the shortcomings of a low detection precision caused by insufficient network attack samples, a power data balancing processing method was proposed. Then, the LightGBM ensemble was constructed to detect network attack events and lock the fault points caused by the attack. At the same time, in the process of gradient boost, the focal loss was introduced to optimize the attention weight of the classifier to the misclassified samples, thus improving the network attack detection precision. Finally, we propose an effective evaluation method of the network attack detection model based on cyber-physical comprehensive consideration. In addition, the cyber-physical power system stability under the action of the network attack detection model is quantitatively analyzed. The experimental results show that the F1 score of network attack detection increases by 16.73%, and the precision increases by 15.67%