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