文章基本信息

标题：Performance Evaluation of Different Kernels for Support Vector Machine Used in Intrusion Detection System
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作者：Md. Al Mehedi Hasan ; Shuxiang Xu ; Mir Md. Jahangir Kabir 等
期刊名称：International Journal of Computer Networks & Communications
印刷版ISSN：0975-2293
电子版ISSN：0974-9322
出版年度：2016
卷号：8
期号：6
页码：39
DOI：10.5121/ijcnc.2016.8604
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：The success of any Intrusion Detection System (IDS) is a complicated problem due to its nonlinearity andthe quantitative or qualitative network traffic data stream with numerous features. As a result, in order toget rid of this problem, several types of intrusion detection methods with different levels of accuracy havebeen proposed which leads the choice of an effective and robust method for IDS as a very important topicin information security. In this regard, the support vector machine (SVM) has been playing an importantrole to provide potential solutions for the IDS problem. However, the practicability of introducing SVM isaffected by the difficulties in selecting appropriate kernel and its parameters. From this viewpoint, thispaper presents the work to apply different kernels for SVM in ID Son the KDD’99 Dataset and NSL-KDDdataset as well as to find out which kernel is the best for SVM. The important deficiency in the KDD’99data set is the huge number of redundant records as observed earlier. Therefore, we have derived a dataset RRE-KDD by eliminating redundant record from KDD’99train and test dataset prior to apply differentkernel for SVM. This RRE-KDD consists of both KDD99Train+ and KDD99 Test+ dataset for trainingand testing purposes, respectively. The way to derive RRE-KDD data set is different from that of NSL-KDDdata set. The experimental results indicate that Laplace kernel can achieve higher detection rate and lowerfalse positive rate with higher precision than other kernel son both RRE-KDD and NSL-KDD datasets. It isalso found that the performances of other kernels are dependent on datasets.
关键词：Intrusion Detection; KDD’99; NSL-KDD; Support Vector Machine; Kernel; Kernel Selection