软件导刊2025，Vol.24Issue(3)：119-126,8.DOI:10.11907/rjdk.241257
基于深度聚类与对比学习的网络入侵检测
Network Intrusion Detection Based on Deep Clustering and Contrastive Learning
摘要
Abstract
In the Internet era,network malicious intrusion events are increasing rapidly,and the importance of network intrusion detection is becoming increasingly prominent.In order to improve the real-time performance of intrusion detection and solve the problem of insufficient la-beled attack samples,a network intrusion detection model based on deep clustering and contrastive learning is proposed.Firstly,the modified VGGNet and LSTM networks are used to extract local and global features,respectively.The clustering optimization model is optimized through examples and comparisons,and abnormal data is detected based on clustering methods.Secondly,taking the raw data packets of network traf-fic as input,the network traffic data is enhanced through optimized data augmentation methods to achieve contrastive learning.Experiments and comparative studies on the CIC-IDS-2017 dataset have shown that the proposed model outperforms other similar methods,and the modi-fied VGGN is more suitable for processing network traffic data.The introduction of LSTM and contrastive learning can both improve model per-formance.关键词
对比学习/深度聚类/卷积神经网络/网络入侵检测/异常检测Key words
contrastive learning/deep clustering/convolutional neural network/network intrusion detection/anomaly detection分类
信息技术与安全科学引用本文复制引用
郭盈盈,张冬梅,李成龙..基于深度聚类与对比学习的网络入侵检测[J].软件导刊,2025,24(3):119-126,8.基金项目
国家自然科学基金项目(62102235) （62102235）
