通信学报2026，Vol.47Issue(1)：184-200,17.DOI:10.11959/j.issn.1000−436x.2026010
基于多尺度卷积和通道注意力机制的网络流量异常检测方法
Network traffic anomaly detection method based on multi-scale convolution and channel attention mechanism
摘要
Abstract
Considering the problems of traditional detection methods limited by weak model representation capabilities and vulnerability to data class imbalance,a network traffic anomaly detection method integrating multi-scale convolution and a channel attention mechanism was proposed.Firstly,a pyramid convolution module was designed to capture multi-scale features,enhancing classification performance.Next,the channel attention mechanism strengthened responses to abnormal traffic-sensitive features,improving discriminability and suppressing noise.Finally,an improved balanced loss function adjusted class weight coefficients to mitigate data imbalance.Extensive experiments on the NSL-KDD and CIC-IDS-2017 datasets demonstrate the proposed method's effectiveness,which achieves high accuracy of 99.45％and 99.95％on the two datasets,respectively,with low false positive rates of only 0.50％and 0.02％.关键词
网络流量异常检测/多尺度卷积/注意力机制/均衡损失函数Key words
network traffic anomaly detection/multi-scale convolution/attention mechanism/balanced loss function分类
信息技术与安全科学引用本文复制引用
付钰,王玉珏,俞艺涵,刘涛涛,安义帅..基于多尺度卷积和通道注意力机制的网络流量异常检测方法[J].通信学报,2026,47(1):184-200,17.基金项目
国家自然科学基金资助项目(No.2022208020,No.2022208010)The National Natural Science Foundation of China(No.2022208020,No.2022208010) （No.2022208020,No.2022208010）
