标题:Research of Hierarchical Intrusion Detection Model Based on Discrete Cellular Neural Networks
作者:Kang Xie;Yixian Yang;Ling Zhang;Wei Li;Yang Xin
作者机构:[Xie, K] College of Information Science and Engineering, Shandong University, Jinan 250100, China;[ Yang, Y] College of Information Science and Engine 更多
通讯作者:Xie, K
来源:Journal of information and computational science
出版年:2013
卷:10
期:17
页码:5569-5578
DOI:10.12733/jics20102446
关键词:Hierarchical Intrusion Detection;Discrete Cellular Neural Networks;Particle Swarm Optimization;Data Classifier
摘要:In this paper, a new method of hierarchical intrusion detection algorithm based on the Discrete Cellular Neural Networks (HDCNN) is put forward to solve the problems of low accuracy and slow speed in the existing intrusion detection algorithm. In order to obtain the template parameters for the HDCNN classifier, we use energy function constraint method to construct a new particle swarm optimization fitness function, jumping out the premature convergence. Emerging evidence has indicated that this new approach is affordable to parallelism and analog VLSI implementation. Experimental results and comparative studies based on the KDD cup 99 data sets are given, show that the proposed model exhibits an excellent performance owing to the higher attack detection rate and shorter processing time.
收录类别:EI;SCOPUS
Scopus被引频次:1
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84889250350&doi=10.12733%2fjics20102446&partnerID=40&md5=525954da7a4e3f41011897ced8932f15
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