| 一种基于粗糙集属性约简的支持向量异常入侵检测方法 |
| |
| 引用本文： | 张义荣,鲜明,肖顺平,王国玉.一种基于粗糙集属性约简的支持向量异常入侵检测方法[J].计算机科学,2006,33(6):64-68. |
| |
| 作者姓名： | 张义荣 鲜明 肖顺平 王国玉 |
| |
| 作者单位： | 国防科技大学电子科学与工程学院,长沙410073;国防科技大学电子科学与工程学院,长沙410073;国防科技大学电子科学与工程学院,长沙410073;国防科技大学电子科学与工程学院,长沙410073 |
| |
| 基金项目： | 国家自然科学基金;国防预研基金 |
| |
| 摘 要： | 实现了一种粗糙集属性约简和支持向量机分类相结合的异常入侵检测方法。针对网络连接记录特征属性高维的特点，采用粗糙集属性约简的方法压缩数据空间，然后采用υ-SVM两分类方法处理约简和正规化后的数据。基于DARPA1998数据源的实验表明，与采用全部属性的υ-SVM两分类方法相比，该方法具有与之相当的分类精度，但有效地降低了检测时间，减少了存储空间。
|
| 关 键 词： | 异常检测 粗糙集理论 属性约简 v-SVM算法 异构值差度量(HVDM) |
An Anomaly Intrusion Detection Technique of Support Vector Machine Based on Rough Set Attribute Reduction |
| |
| ZHANG Yi-Rong,XIAN Ming,XIAO Shun-Ping,WANG Guo-Yu.An Anomaly Intrusion Detection Technique of Support Vector Machine Based on Rough Set Attribute Reduction[J].Computer Science,2006,33(6):64-68. |
| |
| Authors: | ZHANG Yi-Rong XIAN Ming XIAO Shun-Ping WANG Guo-Yu |
| |
| Affiliation: | School of Electronic Science and Engineering, National Univ. of Defense Technology, Changsha 410073 |
| |
| Abstract: | This paper presented the implementation of a hybird anomaly intrusion detection technique based on rough set attribute reduction and support vector machine(SVM). According to the high dimension of network records with feature attributes,the rough set attribute reduction approach is firstly utilized to reducing data space and then the v-SVM algorithm is introduced into processing normalized data set. Experiments on DARPA 1998 data set show that the proposed anomaly detection technique achieves a comparable precise detection rate as the v-SVM algorithm based on all feature attributes,however,evidently decreases detection time as well as storage space. |
| |
| Keywords: | Anomaly detection Rough set theory Attribute reduction v-SVM algorithm Heterogeneous value difference metric(HVDM) |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
|
点击此处可从《计算机科学》下载全文 |
