Intrusion Detection in Software-Defined Networks: Leveraging Deep Reinforcement Learning with Graph Convolutional Networks for Resilient Infrastructure

Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/2528 2018 2018 Intrusion Detection in Software-Defined Networks: Leveraging Deep Reinforcement Learning with Graph Convolutional Networks for Resilient Infrastructure College of Dentistry, University of Al-Ameed, Iraq Mohanad Mohanad Ministry of Higher Education and Scientific Research , Baghdad, Iraq; Visiting Fellow , School of Engineering and Build Environment, Anglia Ruskin University, Chelmsford , UK Firas Hazzaa Medical Instruments techniques Engineering Department, Technical College of Engineering, ‏ Al-Bayan University, Baghdad, Iraq Mohanad Sameer .. Department of Medical Instrumentation Technical Engineering, Medical Technical College, Al-Farahidi University, Baghdad, Iraq Jamal Fadhil .. Symbiosis Institute of Technology (SIT) Pune Campus, Symbiosis International (Deemed University) (SIU), Pune, 412115, Maharashtra, India Ravi Sekhar Symbiosis Institute of Technology (SIT) Pune Campus, Symbiosis International (Deemed University) (SIU), Pune, 412115, Maharashtra, India Pritesh .. Symbiosis Institute of Technology (SIT) Pune Campus, Symbiosis International (Deemed University) (SIU), Pune, 412115, Maharashtra, India Sushma Parihar Protecting Software-Defined Networking (SDN) environments from intrusions and unauthorized access requires a high level of security. Security issues have arisen because of the widespread use of Software-Defined Networking (SDN), especially regarding intrusions that may cause disruptions to network operations by gaining unauthorized access. Intrusion is a danger to an SDN architecture's security, efficacy, and dependability because it involves manipulation or disruption. To improve SDN security through Intrusion Detection Systems (IDS), this study suggests a novel approach that makes use of Graph Convolutional Networks (GCN) and Deep Reinforcement Learning (DRL). The approach, which makes use of the NSL-KDD dataset, shows enhanced performance measures for intrusion detection, such as accuracy (93.8%), recall (93%), F1-score (92%), and precision (94.2%). This work establishes the groundwork for resilient infrastructure against threats and advances the security posture of SDN environments. 2024 2024 78 87 10.54216/FPA.150107 https://www.americaspg.com/articleinfo/3/show/2528