摘要
入侵检测系统可以主动识别恶意流量,是保护网络安全的重要工具.针对网络流量中存在的冗余特征以及现有的入侵检测算法在特征选择过程中存在的不足,提出一种融合对比学习和特征选择的入侵检测模型(contrastive learning and feature selection,CL-FS).利用皮尔逊相关系数(Pearson correlation coefficient,PCCs)对预处理后的网络流量进行相关性分析,过滤掉相似特征;使用自编码器(autoencoder,AE)进行深度特征提取,在提取阶段融入对比学习,减少类间相似性,将提取的新特征和过滤后的特征融合,得到表征能力更强的特征集;利用改进的鸽群算法进行包裹特征选择,根据贝叶斯分类器的性能选择最优特征子集,提高分类精度.在NSL-KDD,UNSW-NB15这2个数据集的实验结果表明,CL-FS模型可以提升分类精度并减少处理时间,在2个数据集上的2分类实验准确率分别为90.45%和88.52%,分类处理时间大约减少为原来的一半.
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.
作者
陈虹
程明佳
金海波
武聪
姜朝议
Chen Hong;Cheng Mingjia;Jin Haibo;Wu Cong;Jiang Chaoyi(College of Software,Liaoning Technical University,Huludao,Liaoning 125105;Institute of Science and Technology,Liaoning Technical University,Fuxin,Liaoning 123099)
出处
《信息安全研究》
CSCD
北大核心
2024年第5期453-461,共9页
Journal of Information Security Research
基金
国家自然科学基金项目(62173171)
辽宁省教育厅科研项目(LJKFZ20220198)。
关键词
对比学习
皮尔逊相关系数
鸽群算法
特征提取
特征选择
contrastive learning
Pearson correlation coefficient
pigeon inspired optimizer
feature extraction
feature selection
