Network intrusion detection using hybrid neural networks

被引：0
|
作者：
Kumar, P. Ganesh [1 ]
Devaraj, D. [1 ]
机构：
[1] Arulmigu Kalasalingam Coll Engn, Krishanankoil 626190, Tamil Nadu, India
来源：
2007 INTERNATIONAL CONFERENCE OF SIGNAL PROCESSING, COMMUNICATIONS AND NETWORKING, VOLS 1 AND 2 | 2006年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Intrusion Detection is a critical process in network security. It is the task of detecting, preventing and possibly reacting to the attack and intrusions in a network based computer systems. This paper presents an intrusion detection system based on Self-Organizing Maps (SOM) and Back Propagation Network (BPN) for visualizing and classifying intrusion. The performance of the proposed Hybrid Neural Network approach is tested using KDD cup' 99 data available in the UCI KDD archive. The proposed approach considers all kinds of attacks under major category (Normal, DOS, Probe,U2R, and R2L) which provides an insightful visualization for network intrusion and works well in detecting different attacks in the considered system.
引用
收藏
页码：563 / +
页数：2
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