标题:Cellular Neural Network-Based Methods for Distributed Network Intrusion Detection
作者:Xie, Kang; Yang, Yixian; Xin, Yang; Xia, Guangsheng
作者机构:[Xie, Kang; Yang, Yixian] Shandong Univ, Coll Informat Sci & Engn, Jinan 250100, Peoples R China.; [Yang, Yixian; Xin, Yang] Beijing Univ Posts & Te 更多
通讯作者:Xie, Kang
通讯作者地址:[Xie, K]Shandong Univ, Coll Informat Sci & Engn, Jinan 250100, Peoples R China.
来源:MATHEMATICAL PROBLEMS IN ENGINEERING
出版年:2015
卷:2015
DOI:10.1155/2015/343050
摘要:According to the problems of current distributed architecture intrusion detection systems (DIDS), a new online distributed intrusion detection model based on cellular neural network (CNN) was proposed, in which discrete-time CNN (DTCNN) was used as weak classifier in each local node and state-controlled CNN (SCCNN) was used as global detection method, respectively. We further proposed a new method for design template parameters of SCCNN via solving Linear Matrix Inequality. Experimental results based on KDDCUP 99 dataset show its feasibility and effectiveness. Emerging evidence has indicated that this new approach is affordable to parallelism and analog very large scale integration (VLSI) implementation which allows the distributed intrusion detection to be performed better.
收录类别:EI;SCOPUS;SCIE
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-s2.0-84925321792&doi=10.1155%2f2015%2f343050&partnerID=40&md5=8b093beb1b797fe12e51ffd3d2b7a915
TOP