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, Hao 2
	2017-07-02
会议日期	October 27, 2017 - October 30, 2017
会议地点	Chengdu, China
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
卷号	2017-October
DOI	10.1109/ICCT.2017.8359987
页码	2042-2045
英文摘要	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.
会议录	International Conference on Communication Technology Proceedings, ICCT
会议录出版者	Institute of Electrical and Electronics Engineers Inc.
语种	英语
内容类型	会议论文
源URL	[http://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
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]. 见:. Chengdu, China. October 27, 2017 - October 30, 2017.