Policy conflict detection in software defined network by using deep learning - Chuanhuang LI; Cheng CHENG; Xiaoyong YUAN; Lijie CEN; Weiming WANG (Dianxin kexue)
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.