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

aspg-3-2528-1791417456 20261007235736 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2024 15 1 Intrusion Detection in Software-Defined Networks: Leveraging Deep Reinforcement Learning with Graph Convolutional Networks for Resilient Infrastructure Fuqdan A. Al-Ibraheemi College of Dentistry, University of Al-Ameed, Iraq Firas Hazzaa Ministry of Higher Education and Scientific Research , Baghdad, Iraq; Visiting Fellow , School of Engineering and Build Environment, Anglia Ruskin University, Chelmsford , UK Mohanad Sameer Jabbar Medical Instruments techniques Engineering Department, Technical College of Engineering, ‏ Al-Bayan University, Baghdad, Iraq Jamal Fadhil Tawfeq Department of Medical Instrumentation Technical Engineering, Medical Technical College, Al-Farahidi University, Baghdad, Iraq Ravi Sekhar Symbiosis Institute of Technology (SIT) Pune Campus, Symbiosis International (Deemed University) (SIU), Pune, 412115, Maharashtra, India Pritesh Shah Symbiosis Institute of Technology (SIT) Pune Campus, Symbiosis International (Deemed University) (SIU), Pune, 412115, Maharashtra, India Sushma Parihar Symbiosis Institute of Technology (SIT) Pune Campus, Symbiosis International (Deemed University) (SIU), Pune, 412115, Maharashtra, India 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 78 87 2528 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.150107 https://www.americaspg.com/journal/3/article/2528 https://www.americaspg.com/storage/01709132291.pdf