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Mathematical Problems in Engineering
Volume 2015, Article ID 343050, 10 pages
http://dx.doi.org/10.1155/2015/343050
Research Article

Cellular Neural Network-Based Methods for Distributed Network Intrusion Detection

1College of Information Science and Engineering, Shandong University, Jinan 250100, China
2Information Security Center, Beijing University of Posts and Telecommunications, Beijing 100876, China
3National Cybernet Security Ltd., Beijing 100088, China

Received 8 July 2014; Accepted 13 January 2015

Academic Editor: Alessandro Gasparetto

Copyright © 2015 Kang Xie et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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 KDD CUP 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.