SVM Intrusion Detection Model Based on Compressed Sampling
Intrusion detection needs to deal with a large amount of data; particularly, the technology of network intrusion detection has to detect all of network data. Massive data processing is the bottleneck of network software and hardware equipment in intrusion detection. If we can reduce the data dimensi...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2016/3095971 |
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| _version_ | 1832561558875537408 |
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| author | Shanxiong Chen Maoling Peng Hailing Xiong Xianping Yu |
| author_facet | Shanxiong Chen Maoling Peng Hailing Xiong Xianping Yu |
| author_sort | Shanxiong Chen |
| collection | DOAJ |
| description | Intrusion detection needs to deal with a large amount of data; particularly, the technology of network intrusion detection has to detect all of network data. Massive data processing is the bottleneck of network software and hardware equipment in intrusion detection. If we can reduce the data dimension in the stage of data sampling and directly obtain the feature information of network data, efficiency of detection can be improved greatly. In the paper, we present a SVM intrusion detection model based on compressive sampling. We use compressed sampling method in the compressed sensing theory to implement feature compression for network data flow so that we can gain refined sparse representation. After that SVM is used to classify the compression results. This method can realize detection of network anomaly behavior quickly without reducing the classification accuracy. |
| format | Article |
| id | doaj-art-400906038b4648c39a2e3d84c76a26ff |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-400906038b4648c39a2e3d84c76a26ff2025-02-03T01:24:39ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552016-01-01201610.1155/2016/30959713095971SVM Intrusion Detection Model Based on Compressed SamplingShanxiong Chen0Maoling Peng1Hailing Xiong2Xianping Yu3College of Computer and Information Science, Southwest University, Chongqing 400715, ChinaChongqing City Management Vocational College, Chongqing 400055, ChinaCollege of Computer and Information Science, Southwest University, Chongqing 400715, ChinaCollege of Computer and Information Science, Southwest University, Chongqing 400715, ChinaIntrusion detection needs to deal with a large amount of data; particularly, the technology of network intrusion detection has to detect all of network data. Massive data processing is the bottleneck of network software and hardware equipment in intrusion detection. If we can reduce the data dimension in the stage of data sampling and directly obtain the feature information of network data, efficiency of detection can be improved greatly. In the paper, we present a SVM intrusion detection model based on compressive sampling. We use compressed sampling method in the compressed sensing theory to implement feature compression for network data flow so that we can gain refined sparse representation. After that SVM is used to classify the compression results. This method can realize detection of network anomaly behavior quickly without reducing the classification accuracy.http://dx.doi.org/10.1155/2016/3095971 |
| spellingShingle | Shanxiong Chen Maoling Peng Hailing Xiong Xianping Yu SVM Intrusion Detection Model Based on Compressed Sampling Journal of Electrical and Computer Engineering |
| title | SVM Intrusion Detection Model Based on Compressed Sampling |
| title_full | SVM Intrusion Detection Model Based on Compressed Sampling |
| title_fullStr | SVM Intrusion Detection Model Based on Compressed Sampling |
| title_full_unstemmed | SVM Intrusion Detection Model Based on Compressed Sampling |
| title_short | SVM Intrusion Detection Model Based on Compressed Sampling |
| title_sort | svm intrusion detection model based on compressed sampling |
| url | http://dx.doi.org/10.1155/2016/3095971 |
| work_keys_str_mv | AT shanxiongchen svmintrusiondetectionmodelbasedoncompressedsampling AT maolingpeng svmintrusiondetectionmodelbasedoncompressedsampling AT hailingxiong svmintrusiondetectionmodelbasedoncompressedsampling AT xianpingyu svmintrusiondetectionmodelbasedoncompressedsampling |