Attack traffic allocation and load balancing mechanism for SDN
To tackle the problem of traditional traffic allocation methods in software-defined networks (SDN) potentially failing to effectively detect distributed denial of service (DDoS) attacks, a traffic allocation and load balancing mechanism for attack traffic was proposed. The traffic allocation problem...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2025-03-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/thesisDetails#10.11959/j.issn.1000-436x.2025034 |
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| _version_ | 1850269609128624128 |
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| author | LI Man ZHOU Huachun XU Qi DENG Shuangxing ZOU Tao ZHANG Ruyun |
| author_facet | LI Man ZHOU Huachun XU Qi DENG Shuangxing ZOU Tao ZHANG Ruyun |
| author_sort | LI Man |
| collection | DOAJ |
| description | To tackle the problem of traditional traffic allocation methods in software-defined networks (SDN) potentially failing to effectively detect distributed denial of service (DDoS) attacks, a traffic allocation and load balancing mechanism for attack traffic was proposed. The traffic allocation problem was modeled as a Markov decision process (MDP), where the reward function included both resource consumption and delay. To optimize the MDP, a load balancing algorithm based on actor-critic networks was developed. This algorithm allocated traffic to different paths based on traffic and network features with the goal of reducing load and latency. The experimental results demonstrate that, under self-generated and public datasets, the proposed method achieves higher reward than the baseline load balancing methods, indicating its superior performance in load balancing. In terms of throughput, it exhibits high stability with a relatively small variation range, fluctuating between 12.95 Mbit/s and 14.83 Mbit/s. Regarding traffic distribution, the traffic is relatively evenly distributed across all paths. In terms of detection performance, the average weighted precision, average weighted recall, and average weighted F1 score are 90%, 92% and 94%, respectively. |
| format | Article |
| id | doaj-art-183003fdf60a4fd4af57779beee0e9c5 |
| institution | OA Journals |
| issn | 1000-436X |
| language | zho |
| publishDate | 2025-03-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-183003fdf60a4fd4af57779beee0e9c52025-08-20T01:53:04ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2025-03-0146749388698026Attack traffic allocation and load balancing mechanism for SDNLI ManZHOU HuachunXU QiDENG ShuangxingZOU TaoZHANG RuyunTo tackle the problem of traditional traffic allocation methods in software-defined networks (SDN) potentially failing to effectively detect distributed denial of service (DDoS) attacks, a traffic allocation and load balancing mechanism for attack traffic was proposed. The traffic allocation problem was modeled as a Markov decision process (MDP), where the reward function included both resource consumption and delay. To optimize the MDP, a load balancing algorithm based on actor-critic networks was developed. This algorithm allocated traffic to different paths based on traffic and network features with the goal of reducing load and latency. The experimental results demonstrate that, under self-generated and public datasets, the proposed method achieves higher reward than the baseline load balancing methods, indicating its superior performance in load balancing. In terms of throughput, it exhibits high stability with a relatively small variation range, fluctuating between 12.95 Mbit/s and 14.83 Mbit/s. Regarding traffic distribution, the traffic is relatively evenly distributed across all paths. In terms of detection performance, the average weighted precision, average weighted recall, and average weighted F1 score are 90%, 92% and 94%, respectively.http://www.joconline.com.cn/thesisDetails#10.11959/j.issn.1000-436x.2025034SDNtraffic allocationload balanceDDoS attack |
| spellingShingle | LI Man ZHOU Huachun XU Qi DENG Shuangxing ZOU Tao ZHANG Ruyun Attack traffic allocation and load balancing mechanism for SDN Tongxin xuebao SDN traffic allocation load balance DDoS attack |
| title | Attack traffic allocation and load balancing mechanism for SDN |
| title_full | Attack traffic allocation and load balancing mechanism for SDN |
| title_fullStr | Attack traffic allocation and load balancing mechanism for SDN |
| title_full_unstemmed | Attack traffic allocation and load balancing mechanism for SDN |
| title_short | Attack traffic allocation and load balancing mechanism for SDN |
| title_sort | attack traffic allocation and load balancing mechanism for sdn |
| topic | SDN traffic allocation load balance DDoS attack |
| url | http://www.joconline.com.cn/thesisDetails#10.11959/j.issn.1000-436x.2025034 |
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