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Sixth International Conference on Parallel and Distributed Computing Applications and Technologies (PDCAT'05)
Intrusion Detection Based on An Improved ART2 Neural Network
Dalian, China
December 05-December 08
ISBN: 0-7695-2405-2
ASCII Text
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Wu Di, Dai Ji, Chi Zhongxian, "Intrusion Detection Based on An Improved ART2 Neural Network," Parallel and Distributed Computing Applications and Technologies, International Conference on, pp. 234-238, Sixth International Conference on Parallel and Distributed Computing Applications and Technologies (PDCAT'05), 2005.
 
 
BibTex
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@article{ 10.1109/PDCAT.2005.257,
author = {Wu Di and Dai Ji and Chi Zhongxian},
title = {Intrusion Detection Based on An Improved ART2 Neural Network},
journal ={Parallel and Distributed Computing Applications and Technologies, International Conference on},
volume = {0},
year = {2005},
isbn = {0-7695-2405-2},
pages = {234-238},
doi = {http://doi.ieeecomputersociety.org/10.1109/PDCAT.2005.257},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Parallel and Distributed Computing Applications and Technologies, International Conference on
TI - Intrusion Detection Based on An Improved ART2 Neural Network
SN - 0-7695-2405-2
SP234
EP238
A1 - Wu Di,
A1 - Dai Ji,
A1 - Chi Zhongxian,
PY - 2005
KW - null
VL - 0
JA - Parallel and Distributed Computing Applications and Technologies, International Conference on
ER -
 
Wu Di, Dalian University of Technology, Dalian,China
Dai Ji, Dalian University of Technology, Dalian,China
Chi Zhongxian, Dalian University of Technology, Dalian,China
An Intrusion detection algorithm based on an improved ART-2 Neural Networks is proposed in this paper. Based on traditional ART-2 neural networks, a prepositive matching system and an amplitude analysis procedure are employed. The prepositive matching system is employed to hasten the pattern matching and provide stable clustering while training the ANN. It also overcomes the limitation of sensibility to noise existing in ART2. The simulation results showed that the algorithm is efficient and precise. The information of the stable clustering can be used to provide supports for decision-making of defining normal and abnormal behavior patterns.
Citation:
Wu Di, Dai Ji, Chi Zhongxian, "Intrusion Detection Based on An Improved ART2 Neural Network," pdcat, pp.234-238, Sixth International Conference on Parallel and Distributed Computing Applications and Technologies (PDCAT'05), 2005
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