计算机与现代化Issue(8)：48-56,9.DOI:10.3969/j.issn.1006-2475.2025.08.007
基于不平衡电力通信流量的入侵检测方法
A Intrusion Detection Method Based on Imbalanced Power Communication Traffic
摘要
Abstract
With the proliferation of the Internet,cybersecurity issues have become increasingly prominent.Ensuring network se-curity 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 selec-tion by calculating the importance of features to identify the most critical ones.Secondly,to tackle the challenge of data imbal-ance,a Gaussian noise-based SMOTE algorithm is employed to augment the communication traffic data,thus enhancing the neu-ral network model's ability to learn and adapt to various situations.Finally,an ensemble adaptive voting algorithm based on clas-sifiers like decision trees,random forests,KNN,and DNN is designed to implement intrusion detection in electric power com-munication 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.关键词
网络安全/机器学习/递归特征消除法/入侵检测/深度神经网络Key words
network security/machine learning/recursive feature elimination/intrusion detection/deep neural network分类
信息技术与安全科学引用本文复制引用
谢善益,王中奥,占聪聪,李兴旺,夏浩然..基于不平衡电力通信流量的入侵检测方法[J].计算机与现代化,2025,(8):48-56,9.基金项目
中国南方电网有限责任公司科技项目(GDKJXM20222701) （GDKJXM20222701）
国家自然科学基金资助项目(62176264) （62176264）
