Intrusion Detection in Power Cyber-Physical Systems Using Denoising Autoencoder and EQL v2 Loss Function

The network connection of the power cyber-physical system (PCPS) makes it easy to become a potential attack target, leading to serious consequences such as paralysis of the power system. The existence of class imbalance and noise in the PCPS network traffic dataset limits the intrusion detection accuracy. In response to the above difficulties, this article combines the denoising autoencoder and the CNN-Attention network to define the AE-CNA NIDS architecture. The BatchSwapNoise method DAE is used to denoise, compress and reconstruct features of the data, and extract local spatiotemporal features in the CNA block formed by CNN and Attention. Multiple CNA blocks are stacked together to comprehensively learn the multi-layer spatiotemporal characteristics of network attack data. In addition, for the network intrusion dataset imbalance problem, equalization loss v2 (EQL v2) is used to balance the weight attention of the minority class. Experimental results show that AE-CNA performs well in terms of accuracy, precision, and recall value, with an accuracy rate of 98.3%, effectively improving intrusion detection performance and minority class detection rate.