ENSEMBLE-LEARNING FRAMEWORK AS A SECURITY MODEL FOR HARDENING THE SECURITY POSTURE OF SDN IN PERSPECTIVE OF CYBER LAW

Abstract

Information is considered the most important resource for any organization and to share this resource requires network usage and management. Network operators are attempting to adapt to the coordination of distinctive sorts of networks while meeting the difficulties of expanding traffic. The customary network tends to be unbending. After the forwarding strategy has been defined, the best method to alter it is to rearrange all the impacted devices. It is lengthy and focuses on adaptation and overcoming the challenges of portability and huge data. System administrators have greater versatility to configure their networks using Software Defined Networking (SDN) in the sight of cyber law. With SDN, network management moves from classifying usefulness, lawfulness as far as low-level device arrangements to building programming that encourages system administration and troubleshooting. However, with the rise in network technology, great concerns of security emerged so high that, nowadays, security is considered the major issue to address in any organization. For this reason, in this paper, we have proposed an ensemble learning framework for hardening the security posture of SDN in prospective of cyber law. The proposed framework is evaluated on two different datasets, and it has exhibited great results as using different techniques together improves the performance.