Distributed abnormal traffic detection method for SDN based on deep learning
Addressing the high computational expenses, congested shared links, and propensity for single-point failures in network devices that can lead to a degradation of software defined network (SDN) service quality or even network paralysis during the execution of large-scale SDN detection tasks by tradit...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2024-11-01
|
| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024199/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841537122135179264 |
|---|---|
| author | WANG Kun FU Yu DUAN Xueyuan YU Yihan LIU Taotao |
| author_facet | WANG Kun FU Yu DUAN Xueyuan YU Yihan LIU Taotao |
| author_sort | WANG Kun |
| collection | DOAJ |
| description | Addressing the high computational expenses, congested shared links, and propensity for single-point failures in network devices that can lead to a degradation of software defined network (SDN) service quality or even network paralysis during the execution of large-scale SDN detection tasks by traditional abnormal traffic detection methods, a distributed abnormal traffic detection method for SDN based on deep learning was proposed. This method constructed a “one-to-many” distributed generative adversarial network (D-VAE-WGAN) with a discriminator deployed on a cloud server and multiple generators deployed on SDN controllers. Utilizing normal traffic samples, collaborative training of the D-VAE-WGAN was completed, resulting in independent abnormal traffic detection proxies on controllers, enabling distributed detection of abnormal traffic within each controller's subnet in a large-scale SDN environment. Experimental results indicate that this method can rapidly and accurately detect abnormal samples in large-scale SDN, outperforming traditional methods in detection metrics such as accuracy and recall rate, and can detect unknown anomalies. |
| format | Article |
| id | doaj-art-9332c94f547e4cb5aa963cdf1424a0cf |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2024-11-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-9332c94f547e4cb5aa963cdf1424a0cf2025-01-14T08:46:18ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2024-11-014511413079134375Distributed abnormal traffic detection method for SDN based on deep learningWANG KunFU YuDUAN XueyuanYU YihanLIU TaotaoAddressing the high computational expenses, congested shared links, and propensity for single-point failures in network devices that can lead to a degradation of software defined network (SDN) service quality or even network paralysis during the execution of large-scale SDN detection tasks by traditional abnormal traffic detection methods, a distributed abnormal traffic detection method for SDN based on deep learning was proposed. This method constructed a “one-to-many” distributed generative adversarial network (D-VAE-WGAN) with a discriminator deployed on a cloud server and multiple generators deployed on SDN controllers. Utilizing normal traffic samples, collaborative training of the D-VAE-WGAN was completed, resulting in independent abnormal traffic detection proxies on controllers, enabling distributed detection of abnormal traffic within each controller's subnet in a large-scale SDN environment. Experimental results indicate that this method can rapidly and accurately detect abnormal samples in large-scale SDN, outperforming traditional methods in detection metrics such as accuracy and recall rate, and can detect unknown anomalies.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024199/deep learningsoftware defined networkdistributedabnormal traffic detection |
| spellingShingle | WANG Kun FU Yu DUAN Xueyuan YU Yihan LIU Taotao Distributed abnormal traffic detection method for SDN based on deep learning Tongxin xuebao deep learning software defined network distributed abnormal traffic detection |
| title | Distributed abnormal traffic detection method for SDN based on deep learning |
| title_full | Distributed abnormal traffic detection method for SDN based on deep learning |
| title_fullStr | Distributed abnormal traffic detection method for SDN based on deep learning |
| title_full_unstemmed | Distributed abnormal traffic detection method for SDN based on deep learning |
| title_short | Distributed abnormal traffic detection method for SDN based on deep learning |
| title_sort | distributed abnormal traffic detection method for sdn based on deep learning |
| topic | deep learning software defined network distributed abnormal traffic detection |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024199/ |
| work_keys_str_mv | AT wangkun distributedabnormaltrafficdetectionmethodforsdnbasedondeeplearning AT fuyu distributedabnormaltrafficdetectionmethodforsdnbasedondeeplearning AT duanxueyuan distributedabnormaltrafficdetectionmethodforsdnbasedondeeplearning AT yuyihan distributedabnormaltrafficdetectionmethodforsdnbasedondeeplearning AT liutaotao distributedabnormaltrafficdetectionmethodforsdnbasedondeeplearning |