基于特征选择的网络入侵检测模型  

Network intrusion detection model based on feature selection

作　　者：姜滨[1] JIANG Bin(Beihua University Teacher′s College,Jilin 132013,China)

机构地区：[1]北华大学师范分院,吉林吉林132013

出　　处：《现代电子技术》2019年第1期87-90,94,共5页Modern Electronics Technique

摘　　要：入侵检测是保证网络安全的重要手段之一,针对当前网络入侵检测模型无法获得理想网络入侵检测效果的难题,设计了基于特征选择的网络入侵检测模型。首先对当前网络入侵检测研究现状进行分析,指出当前网络入侵检测模型存在的不足;然后采集网络状态信息,并提取网络原始特征;最后采用主成分分析选择主要特征,并引入极限学习机建立网络入侵检测的分类器,采用KDD CUP99数据集对模型进行分析,结果表明,所提方法大幅度改善了网络入侵检测率,网络入侵的误检率、漏检率低,网络入侵检测整体效果要显著优于当前其他网络入侵检测模型。The intrusion detection acts as one of the most important ways to ensure the network security.Since the current network intrusion detection model can′t get the desired effect of network intrusion detection,a network intrusion detection model based on feature selection was designed.The research status of the network intrusion detection is analyzed,and then the deficiency of current network intrusion detection model is pointed out.The network state information is acquired.The original network characteristics are extracted,and then the principal component analysis is used to select the important characteristics.The extreme learning machine is introduced to establish the classifier of network intrusion detection.The KDD CUP99dataset is adopted to analyze the model.The analysis results show that the method can improve the network intrusion detection rate greatly,and has low false detection rate and misdetection rate for network intrusion detection,and the overall detection effect of the network intrusion detection model is superior to other network intrusion detection models.

关 键 词：网络入侵 检测模型 特征选择 分类器设计 主成分分析 网络安全 

分 类 号：TN915.08-34[电子电信—通信与信息系统]