融合对比学习和特征选择的入侵检测模型OA北大核心CSTPCD

Intrusion detection systems play a vital role in actively identifying malicious traffic as a crucial tool for safeguarding network security.To address the issue of redundant features in network traffic and the shortcomings of existing intrusion detection algorithms during the feature selection process,we propose an intrusion detection model CL-FS(contrastive learning and feature selection)The model utilizes the Pearson correlation coefficient(PCCs)for analyzing the correlation of pre-processed network traffic and filtering out similar features.Autoencoder(AE)is used for deep feature extraction and in the extraction stage,comparative learning is integrated to reduce the similarity between classes.The extracted new features and filtered features are fused to obtain a feature set with stronger representation ability.To increase classification accuracy,the wrapper feature selection is conducted using the enhanced pigeon swarm algorithm,and the best feature subset is chosen based on how well the Bayesian classifier performs.The experimental results on NSL-KDD and UNSW-NB15 datasets demonstrate that the CL-FS model effectively improves the classification accuracy and reduces the processing time.The accuracy of binary classification experiments on both datasets is 90.45％and 88.52％,respectively,with the classification processing time approximately halved.