Abstract: Abstract: With the proliferation of the Internet， cybersecurity issues have become increasingly prominent. Ensuring network security is crucial within electric power communication networks. However， one challenge faced by these networks is the disparity in the volume between normal and abnormal traffic， as well as the uneven distribution among different types of abnormal traffic. To address this issue， this paper proposes an intrusion detection method for imbalanced electric power communication traffic， named GSMOTE-EAVA. GSMOTE-EAVA firstly utilizes Recursive Feature Elimination for data preprocessing and feature selection by calculating the importance of features to identify the most critical ones. Secondly， to tackle the challenge of data imbalance， a Gaussian noise-based SMOTE algorithm is employed to augment the communication traffic data， thus enhancing the neural network model’s ability to learn and adapt to various situations. Finally， an ensemble adaptive voting algorithm based on classifiers like decision trees， random forests， KNN， and DNN is designed to implement intrusion detection in electric power communication network traffic. Through experiments on the IEC 60870-5-104 intrusion detection dataset and CICIDS2017 dataset， the proposed model significantly improves the detection rate of small sample categories in the dataset under four classifications， and can effectively identify and deal with abnormal traffic in the power communication network.

Key words: Key words: network security, machine learning, recursive feature elimination, intrusion detection, deep neural network