Intelligent detection method on network malicious traffic based on sample enhancement
To address the problem that the existing methods of network traffic anomaly detection not only need a large number of training sets,but also have poor generalization ability,an intelligent detection method on network malicious traffic based on sample enhancement was proposed.The key words were extra...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2020-06-01
|
| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020122/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841539383960797184 |
|---|---|
| author | Tieming CHEN Chengqiang JIN Mingqi LYU Tiantian ZHU |
| author_facet | Tieming CHEN Chengqiang JIN Mingqi LYU Tiantian ZHU |
| author_sort | Tieming CHEN |
| collection | DOAJ |
| description | To address the problem that the existing methods of network traffic anomaly detection not only need a large number of training sets,but also have poor generalization ability,an intelligent detection method on network malicious traffic based on sample enhancement was proposed.The key words were extracted from the training set and the sample of the training set was enhanced based on the strategy of key word avoidance,and the ability for the method to extract the text features from the training set was improved.The experimental results show that,the accuracy of network traffic anomaly detection model and cross dataset can be significantly improved by small training set.Compared with other methods,the proposed method can reduce the computational complexity and achieve better detection ability. |
| format | Article |
| id | doaj-art-cdd4a9e2c52a4fa9b7c6d0f3e8eb596b |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2020-06-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-cdd4a9e2c52a4fa9b7c6d0f3e8eb596b2025-01-14T07:19:08ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2020-06-014112813859734971Intelligent detection method on network malicious traffic based on sample enhancementTieming CHENChengqiang JINMingqi LYUTiantian ZHUTo address the problem that the existing methods of network traffic anomaly detection not only need a large number of training sets,but also have poor generalization ability,an intelligent detection method on network malicious traffic based on sample enhancement was proposed.The key words were extracted from the training set and the sample of the training set was enhanced based on the strategy of key word avoidance,and the ability for the method to extract the text features from the training set was improved.The experimental results show that,the accuracy of network traffic anomaly detection model and cross dataset can be significantly improved by small training set.Compared with other methods,the proposed method can reduce the computational complexity and achieve better detection ability.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020122/sample enhancementanomaly detectiontraffic detectionmachine learning |
| spellingShingle | Tieming CHEN Chengqiang JIN Mingqi LYU Tiantian ZHU Intelligent detection method on network malicious traffic based on sample enhancement Tongxin xuebao sample enhancement anomaly detection traffic detection machine learning |
| title | Intelligent detection method on network malicious traffic based on sample enhancement |
| title_full | Intelligent detection method on network malicious traffic based on sample enhancement |
| title_fullStr | Intelligent detection method on network malicious traffic based on sample enhancement |
| title_full_unstemmed | Intelligent detection method on network malicious traffic based on sample enhancement |
| title_short | Intelligent detection method on network malicious traffic based on sample enhancement |
| title_sort | intelligent detection method on network malicious traffic based on sample enhancement |
| topic | sample enhancement anomaly detection traffic detection machine learning |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020122/ |
| work_keys_str_mv | AT tiemingchen intelligentdetectionmethodonnetworkmalicioustrafficbasedonsampleenhancement AT chengqiangjin intelligentdetectionmethodonnetworkmalicioustrafficbasedonsampleenhancement AT mingqilyu intelligentdetectionmethodonnetworkmalicioustrafficbasedonsampleenhancement AT tiantianzhu intelligentdetectionmethodonnetworkmalicioustrafficbasedonsampleenhancement |