An intelligent zero trust secure framework for software defined networking

Abstract

Software-defined networking (SDN) faces many of the same security threats as traditional networks. The separation of the SDN control plane and data plane makes the controller more vulnerable to cyber attacks. The conventional “perimeter defense” network security model cannot prevent lateral movement attacks caused by malicious insider users or hardware and software vulnerabilities. The “zero trust architecture” has become a new security network model to protect enterprise network security. In this article, we propose an intelligent zero-trust security framework IZTSDN for the software-defined networking by integrating deep learning and zero-trust architecture, which adopts zero-trust architecture to protect every resource and network connection in the network. IZTSDN uses a traffic anomaly detection mode CALSeq2Seql based on a deep learning algorithm to analyze users’ network behavior in real-time and achieve continuous tracking and analysis of users, restrict malicious users from accessing network resources, and realize the dynamic authorization process. Finally, the Mininet simulation platform is extended to build the simulation platform MiniIZTA supporting zero-trust architecture and the proposed security framework IZTSDN is experimentally analyzed. The experimental results show that the IZTSDN security framework can provide about 80.5% of throughput when the network is attacked. The accuracy of abnormal traffic detection reaches 99.56% on the SDN dataset, which verifies that the reliability and availability of the IZTSDN security framework are verified.