Security automation in software defined networks

Abstract

The exponential increase of devices connected to the internet, and the conventional networking operation, based on distributed and static network management, have made networking an incredibly complex task. Software-Defined Networking (SDN) solves the problems arising from the static nature of conventional networking by introducing dynamism to the networking operation. SDN separates the data plane and control plane, centralizes the network control, and automates the network management. In particular, SDN technology is an effective solution to provide security to different network environments. This study solves the security problem in SDN-based networks using state-of-the-art artificial intelligent (AI) techniques. An automated security framework is proposed which integrates two components: 1) Reactive, and 2) Proactive parts. The reactive component uses Deep Learning (DL) to identify complex DDoS threats and Reinforcement Learning (RL) to mitigate them. The proactive component leverages Network Function Virtualization (NFV) to provide scalability to the proposed security framework. Extensive experiments using datasets, simulations, and physical deployments demonstrate the effectiveness of the proposed security automation framework.