水利水电技术（中英文）2025，Vol.56Issue(4)：167-178,12.DOI:10.13928/j.cnki.wrahe.2025.04.014
基于深度学习的水利工控网络流量异常检测方法
Network traffic anomaly detection method for water conservancy industrial control systems based on deep learning
摘要
Abstract
[Objective]This study proposes a network traffic anomaly detection method that addresses the issues of data imbalance,high feature dimensionality,and low detection efficiency in water conservancy industrial control networks.The method integrates an improved Conditional Generative Adversarial Network(ICGAN),Deep Residual Shrinking Network(DRSN),and Long Short-Term Memory Network(LSTM).[Methods]ICGAN was used to construct a balanced network traffic dataset,and a DRSN-LSTM hybrid deep learning model was employed for anomaly detection in network traffic.DRSN was responsible for extracting spatial features,with residual connections addressing network degradation and overfitting issues.The compression and excitation network automatically assigned weight coefficients to each feature map to improve detection performance.Lastly,LSTM extracted temporal features from the data.[Results]The method was tested in the application scenario of the Qinhuai River Wudingmen Sluice Station.The result showed that models trained on the ICGAN-optimized dataset achieved higher traffic classification accuracy than those trained on the original dataset.Overall,DRSN-LSTM achieved an accuracy of 98.76％in detecting network traffic anomalies.P,R,and F1 values for normal data classification were 99.22％,99.69％,and 99.46％,respectively,which outperformed the comparison models in terms of these evaluation indicators.[Conclusion]By integrating the advantages of ICGAN,DRSN,and LSTM algorithms,the anomaly detection method for water conservancy industrial network traffic effectively alleviates the type imbalance in the original dataset,improves the detection ability of abnormal industrial control network traffic,and ensures the safe and stable operation of water conservancy projects.关键词
水利工控/网络流量异常检测/深度学习/条件生成对抗网络/深度残差收缩网络/长短期记忆网络/评价指标Key words
water conservancy industrial control/network traffic anomaly detection/deep learning/conditional generative adversarial networks/deep residual shrinkage network/long short-term memory network/evaluation indicator分类
计算机与自动化引用本文复制引用
马剑波,左翔,丛小飞,叶瑞禄,刘威风..基于深度学习的水利工控网络流量异常检测方法[J].水利水电技术（中英文）,2025,56(4):167-178,12.基金项目
国家重点研发计划(2023YFC3006500) （2023YFC3006500）
江苏省水利科技项目(2022052,2022064) （2022052,2022064）
