| NETWORK INTRUSION DETECTION METHOD BASED ON RS-MSVM |
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| 引用本文： | Xiao Yun Han Chongzhao Zheng Qinghua Zhang Junjie. NETWORK INTRUSION DETECTION METHOD BASED ON RS-MSVM[J]. 电子科学学刊(英文版), 2006, 23(6): 901-905. DOI: 10.1007/s11767-005-0078-x |
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| 作者姓名： | Xiao Yun Han Chongzhao Zheng Qinghua Zhang Junjie |
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| 作者单位： | School of Electronic ＆ Info. Eng., Xi＇an Jiaotong University, Xi＇an 710049, China |
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| 基金项目： | 国家高技术研究发展计划(863计划)，国家重点基础研究发展计划(973计划) |
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| 摘 要： | 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.
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| 关 键 词： | 侵扰检测 粗糙集 支撑向量机制 SVM 内核函数 异类价值微分度量 通信理论 |
| 收稿时间： | 2005-05-12 |
| 修稿时间： | 2005-12-22 |
Network intrusion detection method based on RS-MSVM |
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| Yun Xiao Ph.D.,Chongzhao Han,Qinghua Zheng,Junjie Zhang. Network intrusion detection method based on RS-MSVM[J]. Journal of Electronics, 2006, 23(6): 901-905. DOI: 10.1007/s11767-005-0078-x |
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| Authors: | Yun Xiao Ph.D. Chongzhao Han Qinghua Zheng Junjie Zhang |
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| Affiliation: | School of Electronic & Info. Eng., Xi'an Jiaotong University, Xi'an 710049, China |
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| Abstract: | 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, false negative rate, training time and testing time. |
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| Keywords: | Intrusion detection rough set Support Vector Machine (SVM) Kernel function Heterogeneous Value Difference Metric (HVDM) |
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