Policy conflict detection in software defined network by using deep learning
In OpenFlow-based SDN(software defined network),applications can be deployed through dispatching the flow polices to the switches by the application orchestrator or controller.Policy conflict between multiple applications will affect the actual forwarding behavior and the security of the SDN.With th...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2017-11-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2017305/ |
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| _version_ | 1850073082378584064 |
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| author | Chuanhuang LI Cheng CHENG Xiaoyong YUAN Lijie CEN Weiming WANG |
| author_facet | Chuanhuang LI Cheng CHENG Xiaoyong YUAN Lijie CEN Weiming WANG |
| author_sort | Chuanhuang LI |
| collection | DOAJ |
| description | In OpenFlow-based SDN(software defined network),applications can be deployed through dispatching the flow polices to the switches by the application orchestrator or controller.Policy conflict between multiple applications will affect the actual forwarding behavior and the security of the SDN.With the expansion of network scale of SDN and the increasement of application number,the number of flow entries will increase explosively.In this case,traditional algorithms of conflict detection will consume huge system resources in computing.An intelligent conflict detection approach based on deep learning was proposed which proved to be efficient in flow entries’ conflict detection.The experimental results show that the AUC (area under the curve) of the first level deep learning model can reach 97.04%,and the AUC of the second level model can reach 99.97%.Meanwhile,the time of conflict detection and the scale of the flow table have a linear growth relationship. |
| format | Article |
| id | doaj-art-9c3efd94bcd0418696e331e3bdb20cee |
| institution | DOAJ |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2017-11-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-9c3efd94bcd0418696e331e3bdb20cee2025-08-20T02:46:56ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012017-11-0133273659598843Policy conflict detection in software defined network by using deep learningChuanhuang LICheng CHENGXiaoyong YUANLijie CENWeiming WANGIn OpenFlow-based SDN(software defined network),applications can be deployed through dispatching the flow polices to the switches by the application orchestrator or controller.Policy conflict between multiple applications will affect the actual forwarding behavior and the security of the SDN.With the expansion of network scale of SDN and the increasement of application number,the number of flow entries will increase explosively.In this case,traditional algorithms of conflict detection will consume huge system resources in computing.An intelligent conflict detection approach based on deep learning was proposed which proved to be efficient in flow entries’ conflict detection.The experimental results show that the AUC (area under the curve) of the first level deep learning model can reach 97.04%,and the AUC of the second level model can reach 99.97%.Meanwhile,the time of conflict detection and the scale of the flow table have a linear growth relationship.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2017305/policy conflict detectiondeep learninganomaly detectionSDNOpenFlow |
| spellingShingle | Chuanhuang LI Cheng CHENG Xiaoyong YUAN Lijie CEN Weiming WANG Policy conflict detection in software defined network by using deep learning Dianxin kexue policy conflict detection deep learning anomaly detection SDN OpenFlow |
| title | Policy conflict detection in software defined network by using deep learning |
| title_full | Policy conflict detection in software defined network by using deep learning |
| title_fullStr | Policy conflict detection in software defined network by using deep learning |
| title_full_unstemmed | Policy conflict detection in software defined network by using deep learning |
| title_short | Policy conflict detection in software defined network by using deep learning |
| title_sort | policy conflict detection in software defined network by using deep learning |
| topic | policy conflict detection deep learning anomaly detection SDN OpenFlow |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2017305/ |
| work_keys_str_mv | AT chuanhuangli policyconflictdetectioninsoftwaredefinednetworkbyusingdeeplearning AT chengcheng policyconflictdetectioninsoftwaredefinednetworkbyusingdeeplearning AT xiaoyongyuan policyconflictdetectioninsoftwaredefinednetworkbyusingdeeplearning AT lijiecen policyconflictdetectioninsoftwaredefinednetworkbyusingdeeplearning AT weimingwang policyconflictdetectioninsoftwaredefinednetworkbyusingdeeplearning |