Cellular Neural Network-Based Methods for Distributed Network Intrusion Detection

被引：4

作者：

Xie, Kang ^{[1
]}

Yang, Yixian ^{[1
,2
]}

Xin, Yang ^{[2
]}

Xia, Guangsheng ^{[3
]}

机构：

[1] Shandong Univ, Coll Informat Sci & Engn, Jinan 250100, Peoples R China

[2] Beijing Univ Posts & Telecommun, Informat Secur Ctr, Beijing 100876, Peoples R China

[3] Natl Cybernet Secur Ltd, Beijing 100088, Peoples R China

来源：

MATHEMATICAL PROBLEMS IN ENGINEERING | 2015年 / 2015卷

基金：

中国国家自然科学基金;

关键词：

SYSTEMS; FUSION;

D O I：

10.1155/2015/343050

中图分类号：

T [工业技术];

学科分类号：

08 ;

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.

引用

页数：10

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