DocumentCode :
2691163
Title :
Intrusion Detection Based on RBF Neural Network
Author :
Bi, Jing ; Zhang, Kun ; Cheng, Xiaojing
Author_Institution :
Beijing Univ. of Civil Eng. & Archit., Beijing, China
fYear :
2009
fDate :
16-17 May 2009
Firstpage :
357
Lastpage :
360
Abstract :
Radial basis function (RBF) has been one of the most common neural networks used in the intrusion detection system (IDS). To improve the approximation performance and calculation speed of RBF, we describe a method to deal with the benchmark datasets adopted in the research. It includes converting the string to numeric elements firstly, then omitting the unnecessary data and ensuring that the data has the reasonable range limit. The simulation results built upon Matlab software show that the RBF neural network has better performance than BP neural network.
Keywords :
mathematics computing; radial basis function networks; security of data; Matlab software; RBF neural network; approximation performance; intrusion detection; radial basis function; Civil engineering; Computer networks; Data mining; Electronic commerce; Event detection; Feedforward neural networks; Intrusion detection; Military standards; Neural networks; Radial basis function networks; Intrusion Detection; Network Security; RBF network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Engineering and Electronic Commerce, 2009. IEEC '09. International Symposium on
Conference_Location :
Ternopil
Print_ISBN :
978-0-7695-3686-6
Type :
conf
DOI :
10.1109/IEEC.2009.80
Filename :
5175137
Link To Document :
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