DocumentCode
2732965
Title
An improved intrusion detection system based on neural network
Author
Han, Xiao
Author_Institution
Sch. of Software & Microelectron., Peking Univ., Beijing, China
Volume
1
fYear
2009
fDate
20-22 Nov. 2009
Firstpage
887
Lastpage
890
Abstract
Neural network approach is an advanced methodology used for intrusion detection. Adaptive Resonance Theory (ART) is one kind of neural networks featuring self-organization of stable recognition categories and on-line learning. ART 2-A is a fast clustering algorithm of the ART family. But ART 2-A cannot process categorical values, while network traffic data collected for intrusion detection always contain this kind of data. An improved model of ART 2-A is thus proposed to deal with categorical data properly. To verify the accuracy of this new model, experiments were carried out on the KDD Cup 99 data set. Results showed that the performance of the new model was satisfactory.
Keywords
ART neural nets; pattern clustering; security of data; ART 2-A clustering algorithm; KDD Cup 99 data set; adaptive resonance theory; intrusion detection system; neural network; online learning; stable recognition category; Clustering algorithms; Feedforward neural networks; Intrusion detection; Microelectronics; Neural networks; Protection; Resonance; Subspace constraints; Telecommunication traffic; Traffic control; information security; intrusion detection; neural network;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on
Conference_Location
Shanghai
Print_ISBN
978-1-4244-4754-1
Electronic_ISBN
978-1-4244-4738-1
Type
conf
DOI
10.1109/ICICISYS.2009.5358048
Filename
5358048
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