Network Intrusion Detection Method Based on
Multi-Class Support Vector Machine
Xiao Yun,Han Chongzhao,Zheng Qinghua,Wang Qing
(School of Electronics and Information Engineering,Xi'an Jiaotong University,Xi'an
710049,China)
Abstract:Based on heterogeneous value difference metric(HVDM),a radial basis function
(RBF)named HVDM-RBF, was constructed to deal with heterogeneous network data
directly.Using the experimental data,an improved HVDM-RBF was obtained as a new kernel
function,I-HVDM-RBF, which decreases the number of support vectors and reduces the
workload.The multi-class support vector machine was designed to detect network intrusion
by using one-against-one method and I-HVDM-RBF.Defense Advanced Research Projects Agency
intrusion detection evaluating data was used for detecting.The testing results show that
the detection precision is increased by 3%,the number of support vectors and testing time
are decreased about 268 and 5 minutes respectively by contrast with the Ambwani method and
the detection precisions of denial-of-serve, remote-to-local,and user-to-root attacks are
improved about 73%,19% and 3% respectively compared with the method of Lee,which confirms
the good performance of the proposed method.
Keywords:intrusion detection;support vector machine;kernel function;heterogeneous value
difference metric