Telecommunications Science ›› 2017, Vol. 33 ›› Issue (11): 27-36.doi: 10.11959/j.issn.1000-0801.2017305

• Research and Development • Previous Articles     Next Articles

Policy conflict detection in software defined network by using deep learning

Chuanhuang LI1,Cheng CHENG1,Xiaoyong YUAN2,Lijie CEN1,Weiming WANG1   

  1. 1 School of Information and Electrical Engineering,Zhejiang Gongshang University,Hangzhou 310018,China
    2 LiLAB,University of Florida,Gainesville,Florida 32611,USA
  • Revised:2017-11-10 Online:2017-11-01 Published:2017-12-08
  • Supported by:
    The National High Technology Research and Development Program (863 Program)(2015AA011901);The National Natural Science Foundation of China(61402408);The National Natural Science Foundation of China(61379120);Zhejiang Provincial Natural Science Foundation of China(LY18F010006);Zhejiang’s Key Project of Research and Development Plan(2017C03058)


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.

Key words: policy conflict detection, deep learning, anomaly detection, SDN, OpenFlow

CLC Number: 

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