Title :
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
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;
Conference_Titel :
Education Technology and Computer (ICETC), 2010 2nd International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-6367-1
DOI :
10.1109/ICETC.2010.5529535
