Intelligent Intrusion Detection System Model Using Rough Neural Network

被引：4

作者：

YAN Huaizhi HU Changzhen TAN Huimin Information Security and CounterMeasure Technology Research Center Beijing Institute of Technology Beijing ChinaNational Key Laboratory of Mechatronics Engineering and Control Beijing Institute of Technology Beijing China ^{[1
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机构：

来源：

Wuhan University Journal of Natural Sciences | 2005年 / 01期

关键词：

network security; neural network; intelligent intrusion detection; rough set;

D O I：

暂无

中图分类号：

TP183 [人工神经网络与计算];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A model of intelligent intrusion detection based on rough neural network (RNN), which combines the neural network and rough set, is presented. It works by capturing network packets to identify network intrusions or malicious attacks using RNN with sub-nets. The sub-net is constructed by detection-oriented signatures extracted using rough set theory to detect different intrusions. It is proved that RNN detection method has the merits of adaptive, high universality, high convergence speed, easy upgrading and management.

引用

页码：119 / 122

页数：4

共 5 条

[1]

Network-Based Intrusion Detection Using Neural Networks. Bivens A,Palagiri C,Smith R,et al. Intelligent Engineering Systems through Artificial Neural Networks . 2002

[2]

Internet Intrusion Detection Based on Intelligent Neural Network System. Xiao Ying,Li Tao,Wang Xian-wang,et al. High Technology . 2002

[3]

Comparison of Neo-Fuzzy and Rough Neural Networks. Lingras P. Journal of Information Science . 1998

[4]

Training a Neural-Network Based Intrusion Detector to Recognize Novel Attacks. Lee S C,Heinbuch D V. IEEE Transactions on Systems Man and Cybernetics . 2001

[5]

A New Approach to Intrusion Detection Based on Rough Set Theory. Cai Zhong-min,Guan Xiao-hong,Shao Ping,et al. The Chinese Journal . 2003

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