Research on intrusion detection based on improved combination of K-means and multi-level SVM

doi:10.1109/ICCT.2017.8359987

	Research on intrusion detection based on improved combination of K-means and multi-level SVM
	Xiaofeng, Zhang 1; Xiaohong, Hao2
	2017-07-02
会议名称	17th IEEE International Conference on Communication Technology, ICCT 2017
会议录名称	International Conference on Communication Technology Proceedings, ICCT
卷号	2017-October
页码	2042-2045
会议日期	October 27, 2017 - October 30, 2017
会议地点	Chengdu, China
出版者	Institute of Electrical and Electronics Engineers Inc.
摘要	Aiming at the problem that the traditional network intrusion detection algorithm has the advantages of low detection efficiency and high false alarm rate, a network intrusion detection algorithm based on improved K-means and multi-level SVM is proposed. The algorithm first divides the data to be detected into different clusters with the improved K-means, and marked as normal or abnormal; and then use the multi-level SVM to mark the abnormal cluster for detailed classification, the final realization of the detection of network attacks. The proposed intrusion detection algorithm uses the NSL-KDD data set to simulate the experiment. The results show that the proposed algorithm can improve the network intrusion detection rate and reduce the false alarm rate. It is an effective way of network security protection. © 2017 IEEE.
关键词	Errors Image resolution K-means clustering Network security Signal detection Support vector machines Detailed classification Detection efficiency False alarm rate Intrusion detection algorithms K-means Network intrusion detection NSL-KDD Security protection
DOI	10.1109/ICCT.2017.8359987
收录类别	EI
语种	英语
EI入藏号	20182305271284
EI主题词	Intrusion detection
来源库	Compendex
分类代码	716.1 Information Theory and Signal Processing - 723 Computer Software, Data Handling and Applications
引用统计	无
文献类型	会议论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/118082
专题	电气工程与信息工程学院
作者单位	1.School of Computer and Communication, Lanzhou University of Technology, Lanzhou; 730050, China; 2.School of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou; 730050, China
第一作者单位	兰州理工大学
推荐引用方式 GB/T 7714	Xiaofeng, Zhang,Xiaohong, Hao. Research on intrusion detection based on improved combination of K-means and multi-level SVM[C]:Institute of Electrical and Electronics Engineers Inc.,2017:2042-2045.