1
College of Dentistry, University of Al-Ameed, Iraq
(fuqdanal_ibrahimi@alameed.edu.iq)
2
Ministry of Higher Education and Scientific Research , Baghdad, Iraq; Visiting Fellow , School of Engineering and Build Environment, Anglia Ruskin University, Chelmsford , UK
(fih7600@gmail.com)
3
Medical Instruments techniques Engineering Department, Technical College of Engineering, Al-Bayan University, Baghdad, Iraq
(mohanad.s@albayan.edu.iq)
4
Department of Medical Instrumentation Technical Engineering, Medical Technical College, Al-Farahidi University, Baghdad, Iraq
(jamaltawfeq55@gmail.com)
5
Symbiosis Institute of Technology (SIT) Pune Campus, Symbiosis International (Deemed University) (SIU), Pune, 412115, Maharashtra, India
(ravi.sekhar@sitpune.edu.in)
6
Symbiosis Institute of Technology (SIT) Pune Campus, Symbiosis International (Deemed University) (SIU), Pune, 412115, Maharashtra, India
(pritesh.shah@sitpune.edu.in)
7
Symbiosis Institute of Technology (SIT) Pune Campus, Symbiosis International (Deemed University) (SIU), Pune, 412115, Maharashtra, India
(sushmap@sitpune.edu.in)
Abstract :
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.
Keywords :
Computer Science; Network; Virtual Learning Environment (VLE); Fuzzy-based Convolutional Neural Network (FCNN); Software-Defined Networking (SDN).
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| Style | # |
|---|---|
| MLA | Fuqdan A. Al-Ibraheemi, Firas Hazzaa, Mohanad Sameer Jabbar , Jamal Fadhil Tawfeq , Ravi Sekhar, Pritesh Shah , Sushma Parihar. "Intrusion Detection in Software-Defined Networks: Leveraging Deep Reinforcement Learning with Graph Convolutional Networks for Resilient Infrastructure." Fusion: Practice and Applications, Vol. 15, No. 1, 2024 ,PP. 78-87 (Doi : https://doi.org/10.54216/FPA.150107) |
| APA | Fuqdan A. Al-Ibraheemi, Firas Hazzaa, Mohanad Sameer Jabbar , Jamal Fadhil Tawfeq , Ravi Sekhar, Pritesh Shah , Sushma Parihar. (2024). Intrusion Detection in Software-Defined Networks: Leveraging Deep Reinforcement Learning with Graph Convolutional Networks for Resilient Infrastructure. Journal of Fusion: Practice and Applications, 15 ( 1 ), 78-87 (Doi : https://doi.org/10.54216/FPA.150107) |
| Chicago | Fuqdan A. Al-Ibraheemi, Firas Hazzaa, Mohanad Sameer Jabbar , Jamal Fadhil Tawfeq , Ravi Sekhar, Pritesh Shah , Sushma Parihar. "Intrusion Detection in Software-Defined Networks: Leveraging Deep Reinforcement Learning with Graph Convolutional Networks for Resilient Infrastructure." Journal of Fusion: Practice and Applications, 15 no. 1 (2024): 78-87 (Doi : https://doi.org/10.54216/FPA.150107) |
| Harvard | Fuqdan A. Al-Ibraheemi, Firas Hazzaa, Mohanad Sameer Jabbar , Jamal Fadhil Tawfeq , Ravi Sekhar, Pritesh Shah , Sushma Parihar. (2024). Intrusion Detection in Software-Defined Networks: Leveraging Deep Reinforcement Learning with Graph Convolutional Networks for Resilient Infrastructure. Journal of Fusion: Practice and Applications, 15 ( 1 ), 78-87 (Doi : https://doi.org/10.54216/FPA.150107) |
| Vancouver | Fuqdan A. Al-Ibraheemi, Firas Hazzaa, Mohanad Sameer Jabbar , Jamal Fadhil Tawfeq , Ravi Sekhar, Pritesh Shah , Sushma Parihar. Intrusion Detection in Software-Defined Networks: Leveraging Deep Reinforcement Learning with Graph Convolutional Networks for Resilient Infrastructure. Journal of Fusion: Practice and Applications, (2024); 15 ( 1 ): 78-87 (Doi : https://doi.org/10.54216/FPA.150107) |
| IEEE | Fuqdan A. Al-Ibraheemi, Firas Hazzaa, Mohanad Sameer Jabbar, Jamal Fadhil Tawfeq, Ravi Sekhar, Pritesh Shah, Sushma Parihar, Intrusion Detection in Software-Defined Networks: Leveraging Deep Reinforcement Learning with Graph Convolutional Networks for Resilient Infrastructure, Journal of Fusion: Practice and Applications, Vol. 15 , No. 1 , (2024) : 78-87 (Doi : https://doi.org/10.54216/FPA.150107) |