Two-level feature selection method based on SVM for intrusion detection
To select optimized features for intrusion detection，a two-level feature selection method based on support vector machine was proposed.This method set an evaluation index named feature evaluation value for feature selection，which was the ratio of the detection rate and false alarm rate.Firstly，this...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2015-04-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2015127/ |
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| _version_ | 1841539659830657024 |
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| author | Xiao-nian WU Xiao-jin PENG Yu-yang YANG Kun FANG |
| author_facet | Xiao-nian WU Xiao-jin PENG Yu-yang YANG Kun FANG |
| author_sort | Xiao-nian WU |
| collection | DOAJ |
| description | To select optimized features for intrusion detection，a two-level feature selection method based on support vector machine was proposed.This method set an evaluation index named feature evaluation value for feature selection，which was the ratio of the detection rate and false alarm rate.Firstly，this method filtrated noise and irrelevant features to reduce the feature dimension respectively by Fisher score and information gain in the filtration mode.Then，a crossing feature subset was obtained based on the above two filtered feature sets.And combining support vector machine，the sequential backward selection algorithm in the wrapper mode was used to select the optimal feature subset from the crossing feature subset.The simulation test results show that，the better classification performance is obtained according to the selected optimal feature subset，and the modeling time and testing time of the system are reduced effectively. |
| format | Article |
| id | doaj-art-6d834a9b4c884d5a897f010e6b492fc8 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2015-04-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-6d834a9b4c884d5a897f010e6b492fc82025-01-14T06:46:07ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2015-04-0136192659692124Two-level feature selection method based on SVM for intrusion detectionXiao-nian WUXiao-jin PENGYu-yang YANGKun FANGTo select optimized features for intrusion detection，a two-level feature selection method based on support vector machine was proposed.This method set an evaluation index named feature evaluation value for feature selection，which was the ratio of the detection rate and false alarm rate.Firstly，this method filtrated noise and irrelevant features to reduce the feature dimension respectively by Fisher score and information gain in the filtration mode.Then，a crossing feature subset was obtained based on the above two filtered feature sets.And combining support vector machine，the sequential backward selection algorithm in the wrapper mode was used to select the optimal feature subset from the crossing feature subset.The simulation test results show that，the better classification performance is obtained according to the selected optimal feature subset，and the modeling time and testing time of the system are reduced effectively.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2015127/intrusion detectionfeature selectionsupport vector machineFisher scoresequential backward selection |
| spellingShingle | Xiao-nian WU Xiao-jin PENG Yu-yang YANG Kun FANG Two-level feature selection method based on SVM for intrusion detection Tongxin xuebao intrusion detection feature selection support vector machine Fisher score sequential backward selection |
| title | Two-level feature selection method based on SVM for intrusion detection |
| title_full | Two-level feature selection method based on SVM for intrusion detection |
| title_fullStr | Two-level feature selection method based on SVM for intrusion detection |
| title_full_unstemmed | Two-level feature selection method based on SVM for intrusion detection |
| title_short | Two-level feature selection method based on SVM for intrusion detection |
| title_sort | two level feature selection method based on svm for intrusion detection |
| topic | intrusion detection feature selection support vector machine Fisher score sequential backward selection |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2015127/ |
| work_keys_str_mv | AT xiaonianwu twolevelfeatureselectionmethodbasedonsvmforintrusiondetection AT xiaojinpeng twolevelfeatureselectionmethodbasedonsvmforintrusiondetection AT yuyangyang twolevelfeatureselectionmethodbasedonsvmforintrusiondetection AT kunfang twolevelfeatureselectionmethodbasedonsvmforintrusiondetection |