NETWORK INTRUSION DETECTION METHOD BASED ON RS-MSVM

A new method called RS-MSVM (Rough Set and Multi-class Support Vector Machine) is pro-posed for network intrusion detection. This method is based on rough set followed by MSVM for attribute re-duction and classification respectively. The number of attributes of the network data used in this paper is re-duced from 41 to 30 using rough set theory. The kernel function of HVDM-RBF (Heterogeneous Value Dif-ference Metric Radial Basis Function), based on the heterogeneous value difference metric of heterogeneous datasets, is constructed for the heterogeneous network data. HVDM-RBF and one-against-one method are ap-plied to build MSVM. DARPA (Defense Advanced Research Projects Agency) intrusion detection evaluating data were used in the experiment. The testing results show that our method outperforms other methods men-tioned in this paper on six aspects: detection accuracy, number of support vectors, false positive rate, false negative rate, training time and testing time.

A new method called RS-MSVM （Rough Set and Multi-class Support Vector Machine） is proposed for network intrusion detection. This method is based on rough set followed by MSVM for attribute reduction and classification respectively, The number of attributes of the network data used in this paper is reduced from 41 to 30 using rough set theory. The kernel function of HVDM-RBF （Heterogeneous Value Difference Metric Radial Basis Function）, based on the heterogeneous value difference metric of heterogeneous datasets, is constructed for the heterogeneous network data. HVDM-RBF and one-against-one method are applied to build MSVM. DARPA （Defense Advanced Research Projects Agency） intrusion detection evaluating data were used in the experiment. The testing results show that our method outperforms other methods mentioned in this paper on six aspects： detection accuracy, number of support vectors, false positive rate, falsc negative rate, training time and testing time.