摘要
为提高入侵检测效率,需要对数据进行特征提取以降低数据维度。结合信息增益(IG)和主成分分析(PCA),提出一种网络入侵检测方法。通过IG提取分类能力强的属性特征,利用PCA对其降维,并采用NaiveBayes进行分类检测。对数据集KDDCUP99进行测试,结果表明,该方法的检测率为94.5%,高于PCA-LDA、FPCA、KPCA方法。
In order to improve the efficiency of intrusion detection,it is necessary to extract the features of data to reduce the data dimensions.This paper proposes a network intrusion detection method by combining Information Gain(IG) and Principal Components Analysis(PCA).The method uses IG to extract the attribute features with strong classification ability,uses PCA to reduce the dimension of the feature data,and uses Naive Bayes method for classification and detection.The test results of the data set KDDCUP99 show that the detection rate of the method is 94.5%,which is much higher than those of PCA-LDA,FPCA,and KPCA methods.
作者
王旭仁
马慧珍
冯安然
许祎娜
WANG Xuren;MA Huizhen;FENG Anran;XU Yi’na(College of Information Engineering,Capital Normal University,Beijing 100048,China;Key Laboratory of Network Assessment Technology,Institute of Information Engineering, Chinese Academy of Sciences,Beijing 100093,China)
出处
《计算机工程》
CAS
CSCD
北大核心
2019年第6期175-180,共6页
Computer Engineering
基金
国家自然科学基金(61373161)
中国科学院信息工程研究所中国科学院网络测评技术重点实验室开放课题(201710)
关键词
信息增益
主成分分析
入侵检测
特征提取
降维
Information Gain(IG)
Principal Components Analysis(PCA)
intrusion detection
feature extraction
dimension reduction
