Network Anomaly Detection System with Optimized DS Evidence Theory
Network anomaly detection has been focused on by more people with the fast development of computer network. Some researchers utilized fusion method and DS evidence theory to do network anomaly detection but with low performance, and they did not consider features of network—complicated and varied. T...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/753659 |
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| _version_ | 1832558598771703808 |
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| author | Yuan Liu Xiaofeng Wang Kaiyu Liu |
| author_facet | Yuan Liu Xiaofeng Wang Kaiyu Liu |
| author_sort | Yuan Liu |
| collection | DOAJ |
| description | Network anomaly detection has been focused on by more people with the fast development of computer network. Some researchers utilized fusion method and DS evidence theory to do network anomaly detection but with low performance, and they did not consider features of network—complicated and varied. To achieve high detection rate, we present a novel network anomaly detection system with optimized Dempster-Shafer evidence theory (ODS) and regression basic probability assignment (RBPA) function. In this model, we add weights for each senor to optimize DS evidence theory according to its previous predict accuracy. And RBPA employs sensor’s regression ability to address complex network. By four kinds of experiments, we find that our novel network anomaly detection model has a better detection rate, and RBPA as well as ODS optimization methods can improve system performance significantly. |
| format | Article |
| id | doaj-art-6ffb8b3b654149f8ae7b554fe688daa7 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-6ffb8b3b654149f8ae7b554fe688daa72025-02-03T01:32:01ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/753659753659Network Anomaly Detection System with Optimized DS Evidence TheoryYuan Liu0Xiaofeng Wang1Kaiyu Liu2School of Digital Media, Jiangnan University, Wuxi, Jiangsu 214122, ChinaSchool of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu 214122, ChinaSchool of Digital Media, Jiangnan University, Wuxi, Jiangsu 214122, ChinaNetwork anomaly detection has been focused on by more people with the fast development of computer network. Some researchers utilized fusion method and DS evidence theory to do network anomaly detection but with low performance, and they did not consider features of network—complicated and varied. To achieve high detection rate, we present a novel network anomaly detection system with optimized Dempster-Shafer evidence theory (ODS) and regression basic probability assignment (RBPA) function. In this model, we add weights for each senor to optimize DS evidence theory according to its previous predict accuracy. And RBPA employs sensor’s regression ability to address complex network. By four kinds of experiments, we find that our novel network anomaly detection model has a better detection rate, and RBPA as well as ODS optimization methods can improve system performance significantly.http://dx.doi.org/10.1155/2014/753659 |
| spellingShingle | Yuan Liu Xiaofeng Wang Kaiyu Liu Network Anomaly Detection System with Optimized DS Evidence Theory The Scientific World Journal |
| title | Network Anomaly Detection System with Optimized DS Evidence Theory |
| title_full | Network Anomaly Detection System with Optimized DS Evidence Theory |
| title_fullStr | Network Anomaly Detection System with Optimized DS Evidence Theory |
| title_full_unstemmed | Network Anomaly Detection System with Optimized DS Evidence Theory |
| title_short | Network Anomaly Detection System with Optimized DS Evidence Theory |
| title_sort | network anomaly detection system with optimized ds evidence theory |
| url | http://dx.doi.org/10.1155/2014/753659 |
| work_keys_str_mv | AT yuanliu networkanomalydetectionsystemwithoptimizeddsevidencetheory AT xiaofengwang networkanomalydetectionsystemwithoptimizeddsevidencetheory AT kaiyuliu networkanomalydetectionsystemwithoptimizeddsevidencetheory |