Network traffic detection based on multi-resolution low rank model
Because network traffic was usually characterized by its higher-dimensional features,related detectors and classifiers for identifying traffic anomalies were suffering the increased complexity.Several key observations given by existing studies showed that network anomalies were distributed typically...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2012-01-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/1000-436X(2012)01-0182-09/ |
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| _version_ | 1841539947129995264 |
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| author | Guo-zhen CHENG Dong-nian CHENG Ding-jiu YU |
| author_facet | Guo-zhen CHENG Dong-nian CHENG Ding-jiu YU |
| author_sort | Guo-zhen CHENG |
| collection | DOAJ |
| description | Because network traffic was usually characterized by its higher-dimensional features,related detectors and classifiers for identifying traffic anomalies were suffering the increased complexity.Several key observations given by existing studies showed that network anomalies were distributed typically in a sparse way,and each of anomalies was essentially characterized by its lower-dimensional features.Based on this important finding,a novel model detecting traffic anomalies—multi-resolution low rank (MRLR) was developed.The proposed MRLR allowed us to dynamically filter the “proper”feature sets and then to classify anomalies accurately.The validation shows that MRLR can accurately reduce the dimensions of flow features to lower than 10%,on the other hand,the complexity of MRLR-classifiers is O(n). |
| format | Article |
| id | doaj-art-c6745fbab6294b83b7fa96ebc1bcbbc3 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2012-01-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-c6745fbab6294b83b7fa96ebc1bcbbc32025-01-14T06:31:03ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2012-01-013318219059659856Network traffic detection based on multi-resolution low rank modelGuo-zhen CHENGDong-nian CHENGDing-jiu YUBecause network traffic was usually characterized by its higher-dimensional features,related detectors and classifiers for identifying traffic anomalies were suffering the increased complexity.Several key observations given by existing studies showed that network anomalies were distributed typically in a sparse way,and each of anomalies was essentially characterized by its lower-dimensional features.Based on this important finding,a novel model detecting traffic anomalies—multi-resolution low rank (MRLR) was developed.The proposed MRLR allowed us to dynamically filter the “proper”feature sets and then to classify anomalies accurately.The validation shows that MRLR can accurately reduce the dimensions of flow features to lower than 10%,on the other hand,the complexity of MRLR-classifiers is O(n).http://www.joconline.com.cn/zh/article/doi/1000-436X(2012)01-0182-09/anomaly detectionfeature filteringmulti-resolution analysislow-rank distribution |
| spellingShingle | Guo-zhen CHENG Dong-nian CHENG Ding-jiu YU Network traffic detection based on multi-resolution low rank model Tongxin xuebao anomaly detection feature filtering multi-resolution analysis low-rank distribution |
| title | Network traffic detection based on multi-resolution low rank model |
| title_full | Network traffic detection based on multi-resolution low rank model |
| title_fullStr | Network traffic detection based on multi-resolution low rank model |
| title_full_unstemmed | Network traffic detection based on multi-resolution low rank model |
| title_short | Network traffic detection based on multi-resolution low rank model |
| title_sort | network traffic detection based on multi resolution low rank model |
| topic | anomaly detection feature filtering multi-resolution analysis low-rank distribution |
| url | http://www.joconline.com.cn/zh/article/doi/1000-436X(2012)01-0182-09/ |
| work_keys_str_mv | AT guozhencheng networktrafficdetectionbasedonmultiresolutionlowrankmodel AT dongniancheng networktrafficdetectionbasedonmultiresolutionlowrankmodel AT dingjiuyu networktrafficdetectionbasedonmultiresolutionlowrankmodel |