DocumentCode
3062471
Title
Intrusion Detection Based on An Improved ART2 Neural Network
Author
Di, Wu ; Ji, Dai ; Zhongxian, Chi
Author_Institution
Dalian University of Technology, Dalian,China
fYear
2005
fDate
05-08 Dec. 2005
Firstpage
234
Lastpage
238
Abstract
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.
Keywords
Artificial neural networks; Clustering algorithms; Computer science; Data security; Engines; Information processing; Information security; Intrusion detection; Neural networks; Pattern matching;
fLanguage
English
Publisher
ieee
Conference_Titel
Parallel and Distributed Computing, Applications and Technologies, 2005. PDCAT 2005. Sixth International Conference on
Print_ISBN
0-7695-2405-2
Type
conf
DOI
10.1109/PDCAT.2005.257
Filename
1578904
Link To Document