Anomaly intrusion detection based on modified SVM
A modified SVM multi-classification algorithm integrated with discriminant analysis (D-SVM) was pro-posed,which could solve the problem of low detection accuracy and high false alarm rate caused by unbalanced datasets.For a multi-classification problem could be divided into several binary classifica...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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POSTS&TELECOM PRESS Co., LTD
2016-08-01
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| Series: | 网络与信息安全学报 |
| Subjects: | |
| Online Access: | http://www.infocomm-journal.com/cjnis/CN/10.11959/j.issn.2096-109x.2016.00092 |
| _version_ | 1811233415935033344 |
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| author | Hui ZHANG Cheng LIU |
| author_facet | Hui ZHANG Cheng LIU |
| author_sort | Hui ZHANG |
| collection | DOAJ |
| description | A modified SVM multi-classification algorithm integrated with discriminant analysis (D-SVM) was pro-posed,which could solve the problem of low detection accuracy and high false alarm rate caused by unbalanced datasets.For a multi-classification problem could be divided into several binary classification problems,D-SVM could not only have the virtue of high detection accuracy,but also have a low false alarm rate even confronted with unbalanced datasets.Experiments based on KDD99 dataset verify the feasibility and validity of the integrated ap-proach.Results show that when confronted with multi-classification problems,D-SVM could achieve a high detec-tion accuracy and low false alarm rate even when SVM alone fails because of the unbalanced datasets. |
| first_indexed | 2024-04-12T11:20:04Z |
| format | Article |
| id | doaj.art-5788381955ac48d69c2b5948911ede97 |
| institution | Directory Open Access Journal |
| issn | 2096-109X |
| language | English |
| last_indexed | 2024-04-12T11:20:04Z |
| publishDate | 2016-08-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 网络与信息安全学报 |
| spelling | doaj.art-5788381955ac48d69c2b5948911ede972022-12-22T03:35:24ZengPOSTS&TELECOM PRESS Co., LTD网络与信息安全学报2096-109X2016-08-0128687310.11959/j.issn.2096-109x.2016.00092Anomaly intrusion detection based on modified SVMHui ZHANG0Cheng LIU1Special Reconnaissance Team of Xinjiang Public Security Bureau,Urumpi 830000,ChinaNational Computer Network Emergency Response Technical Team/Coordination Center of China,Beijing 100029,ChinaA modified SVM multi-classification algorithm integrated with discriminant analysis (D-SVM) was pro-posed,which could solve the problem of low detection accuracy and high false alarm rate caused by unbalanced datasets.For a multi-classification problem could be divided into several binary classification problems,D-SVM could not only have the virtue of high detection accuracy,but also have a low false alarm rate even confronted with unbalanced datasets.Experiments based on KDD99 dataset verify the feasibility and validity of the integrated ap-proach.Results show that when confronted with multi-classification problems,D-SVM could achieve a high detec-tion accuracy and low false alarm rate even when SVM alone fails because of the unbalanced datasets.http://www.infocomm-journal.com/cjnis/CN/10.11959/j.issn.2096-109x.2016.00092anomaly detectionnon-parametric testsvm classifierunbalanced datasetsdiscriminant analysis |
| spellingShingle | Hui ZHANG Cheng LIU Anomaly intrusion detection based on modified SVM 网络与信息安全学报 anomaly detection non-parametric test svm classifier unbalanced datasets discriminant analysis |
| title | Anomaly intrusion detection based on modified SVM |
| title_full | Anomaly intrusion detection based on modified SVM |
| title_fullStr | Anomaly intrusion detection based on modified SVM |
| title_full_unstemmed | Anomaly intrusion detection based on modified SVM |
| title_short | Anomaly intrusion detection based on modified SVM |
| title_sort | anomaly intrusion detection based on modified svm |
| topic | anomaly detection non-parametric test svm classifier unbalanced datasets discriminant analysis |
| url | http://www.infocomm-journal.com/cjnis/CN/10.11959/j.issn.2096-109x.2016.00092 |
| work_keys_str_mv | AT huizhang anomalyintrusiondetectionbasedonmodifiedsvm AT chengliu anomalyintrusiondetectionbasedonmodifiedsvm |