Network intrusion detection by a hybrid method of rough set and RBF neural network

Author

Li-Zhong, Lin ; Zhi-Guo, Liu ; Xian-Hui, Duan

Author_Institution

ShiJiaZhuang Coll., Shijiazhuang, China

Volume

3

fYear

2010

fDate

22-24 June 2010

Abstract

In order to detect the intrusion for computer network accurately, the network intrusion detection method should be developed continuously. The hybrid method of rough set and RBF neural network is presented to network intrusion detection. The collected 390 cases in KDD-CUP99 applied to research the performance of rough set and RBF neural network compared with RBF neural network, BP neural network. The collected 390 cases include 200 normal data, 50 Probe fault data, 40 U2R fault data, 50 DoS fault data and 50 R2L fault data. It is indicated that the detection accuracies of rough set-RBF neural network are higher than those of normal RBF neural network and BP neural network.

Keywords

neural nets; radial basis function networks; rough set theory; security of data; DoS fault data; KDD-CUP99; R2L fault data; RBF neural network; U2R fault data; computer network; network intrusion detection method; probe fault data; rough set; Computer networks; Computer science education; Educational institutions; Educational technology; Feedforward neural networks; Feedforward systems; Intrusion detection; Neural networks; Neurons; Probes; RBF neural network; fault data; network intrusion; rough set;

fLanguage

English

Publisher

ieee

Conference_Titel

Education Technology and Computer (ICETC), 2010 2nd International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-4244-6367-1

Type

conf

DOI

10.1109/ICETC.2010.5529535

Filename

5529535