Abstract

Abstract Conventional power network attack detection methods mainly use KKT (Karush Kuhn Tucker) Kuentak condition to filter the characteristics of hyperplane intrusion attacks, which is vulnerable to the effect of missing sample category identification, resulting in poor detection performance indicators. Therefore, a power system network attack detection method based on a convolutional gated recurrent neural network is proposed. That is, the convolutional gated recurrent neural network is used to extract the characteristics of network attack detection in the power system, and a hybrid detection model of network attack in the power system is constructed, thus completing the network attack detection in the power system. The experimental results show that the detection performance indicators of the designed power system network attack detection method are all good, reliable, and have certain application values. It has made certain contributions to improving the security of power system operations and reducing the risk of network intrusion.