Submit Your Paper
  1. Home
  2. Journals
  3. Fusion: Practice and Applications

Fusion: Practice and Applications

Journal DOI

https://doi.org/10.54216/FPA

Submit Your Paper

2692-4048ISSN (Online) 2770-0070ISSN (Print)
Submit Your Paper

Journal Menu

Homepage Journal Overview Editorial Board Submitting Articles Indexing & Abstracting

Journal Volumes

Fusion: Practice and Applications

Volume 15 , Issue 1 , PP: 78-87, 2024 | Cite this article as | XML | Html | PDF | Full Length Article

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

Fuqdan A. Al-Ibraheemi 1 , Firas Hazzaa 2 , Mohanad Sameer Jabbar 3 * , Jamal Fadhil Tawfeq 4 , Ravi Sekhar 5 , Pritesh Shah 6 , Sushma Parihar 7

  • 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)

    • Doi: https://doi.org/10.54216/FPA.150107

    Received: August 02, 2023 Revised: December 17, 2023 Accepted: February 15, 2024
    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).

    References

    [1]     Bhardwaj, A., Tyagi, R., Sharma, N., Khare, A., Punia, M.S. and Garg, V.K., “Network intrusion detection in software-defined networking with self-organized constraint-based intelligent learning framework.” Measurement: Sensors, 24, p.100580. 2022.

    [2]     Abou El Houda, Z., Senhaji Hafid, A. and Khoukhi, L., “A novel unsupervised learning method for intrusion detection in software-defined networks.” In Computational Intelligence in Recent Communication Networks (pp. 103-117). Cham: Springer International Publishing. 2021.

    [3]     Girdler, T. and Vassilakis, V.G., “Implementing an intrusion detection and prevention system using Software-Defined Networking: Defending against ARP spoofing attacks and Blacklisted MAC Addresses.” Computers & Electrical Engineering, 90, p.106990. 2021.

    [4]     Kumar, C., Biswas, S., Ansari, M.S.A. and Govil, M.C., “Nature-inspired intrusion detection system for protecting software-defined networks controller.” Computers & Security, 134, p.103438. 2023.

    [5]     Shaji, N.S., Muthalagu, R. and Pawar, P.M. “SD-IIDS: intelligent intrusion detection system for software-defined networks.” Multimedia Tools and Applications, pp.1-33. 2023.

    [6]     Ahmad, A.A., “Solution Model for Intrusion Detection in Software Defined Networking (SDN) using Machine Learning.” 2021.

    [7]     Alshammri, G.H., Samha, A.K., Hemdan, E.E.D., Amoon, M. and El-Shafai, W., “An efficient intrusion detection framework in software-defined networking for cybersecurity applications.” CMC-Comput. Mater. Contin, 72, pp.3529-3548. 2022.

    [8]     Bour, H., Abolhasan, M., Jafarizadeh, S., Lipman, J. and Makhdoom, I., “A multi-layered intrusion detection system for software defined networking.” Computers and Electrical Engineering, 101, p.108042. 2022.

    [9]     Scaranti, G.F., Carvalho, L.F., Junior, S.B., Lloret, J. and Proença Jr, M.L., “Unsupervised online anomaly detection in Software Defined Network environments.” Expert Systems with Applications, 191, p.116225. 2022.

    [10]   Vinayakumar, R., Alazab, M., Soman, K.P., Poornachandran, P., Al-Nemrat, A. and Venkatraman, S., “Deep learning approach for intelligent intrusion detection system.” Ieee Access, 7, pp.41525-41550. 2019.

    [11]   Tang, T.A., Mhamdi, L., McLernon, D., Zaidi, S.A.R., Ghogho, M. and El Moussa, F., “DeepIDS: Deep learning approach for intrusion detection in software defined networking.” Electronics, 9(9), p.1533. 2020.

    [12]   Satheesh, N., Rathnamma, M.V., Rajeshkumar, G., Sagar, P.V., Dadheech, P., Dogiwal, S.R., Velayutham, P. and Sengan, S., “Flow-based anomaly intrusion detection using machine learning model with software defined networking for OpenFlow network.” Microprocessors and Microsystems, 79, p.103285, 2020.

    [13]   Jayasri, P., Atchaya, A., Parveen, M.S. and Ramprasath, J., “Intrusion detection system in software defined networks using machine learning approach.” International Journal of Advanced Engineering Research and Science, 8(8), 2021.

    [14]   Janabi, A.H., Kanakis, T. and Johnson, M., “Overhead reduction technique for software-defined network based intrusion detection systems.” IEEE Access, 10, pp.66481-66491, 2022.

    [15]   Dey, S.K. and Rahman, M.M., “Effects of machine learning approach in flow-based anomaly detection on software-defined networking.” Symmetry, 12(1), p.7, 2019.

    [16]    Naqash, T., Shah, S.H. and Islam, M.N.U., “Statistical Analysis Based Intrusion Detection System for Ultra-High-Speed Software Defined Network.” International Journal of Parallel Programming, 50(1), pp.89-114, 2022.

    [17]   Alshammari, T.M. and Alserhani, F.M., “Scalable and Robust Intrusion Detection System to Secure the IoT Environments using Software Defined Networks (SDN) Enabled Architecture.” Int. J. Comput. Networks Appl, 9(6), pp.678-688, 2022.

    [18]   Latah, M. and Toker, L., “An efficient flow-based multi-level hybrid intrusion detection system for software-defined networks.” CCF Transactions on Networking, 3(3-4), pp.261-271, 2020.

    [19]    Bocu, R. and Iavich, M., “Real-Time Intrusion Detection and Prevention System for 5G and beyond Software-Defined Networks.” Symmetry, 15(1), p.110, 2022.

    [20]   Li, W., Wang, Y., Jin, Z., Yu, K., Li, J. and Xiang, Y., “Challenge-based collaborative intrusion detection in software-defined networking: an evaluation.” Digital Communications and Networks, 7(2), pp.257-263, 2021.

    [21]   S. A. Shawkat, B. A. Tuama, and I. Al Barazanchi, “Proposed system for data security in distributed computing in using triple data encryption standard and Rivest Shamir Adlemen,” Int. J. Electr. Comput. Eng., vol. 12, no. 6, pp. 6496–6505, 2022, doi: 10.11591/ijece.v12i6.pp6496-6505.

    [22]   F. Hazzaa, S. Yousef, E. Sanchez and M. Cirstea, "Lightweight and Low-Energy Encryption Scheme for Voice over Wireless Devices," IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, Washington, DC, USA, 2018, pp. 2992-2997, doi: 10.1109/IECON.2018.8591451.

    [23]   F. Hazzaa, S. Yousef, N. H. Ali and E. Sanchez, "The Effect of Nodes Density on Real Time Traffic in Mobile Ad Hoc Network," 2019 IEEE 12th International Conference on Global Security, Safety and Sustainability (ICGS3), London, UK, 2019, pp. 209-212, doi: 10.1109/ICGS3.2019.8688314

    Cite This Article As :
    A., Fuqdan. , Hazzaa, Firas. , Sameer, Mohanad. , Fadhil, Jamal. , Sekhar, Ravi. , Shah, Pritesh. , Parihar, Sushma. Intrusion Detection in Software-Defined Networks: Leveraging Deep Reinforcement Learning with Graph Convolutional Networks for Resilient Infrastructure. Fusion: Practice and Applications, vol. , no. , 2024, pp. 78-87. DOI: https://doi.org/10.54216/FPA.150107
    A., F. Hazzaa, F. Sameer, M. Fadhil, J. Sekhar, R. Shah, P. Parihar, S. (2024). Intrusion Detection in Software-Defined Networks: Leveraging Deep Reinforcement Learning with Graph Convolutional Networks for Resilient Infrastructure. Fusion: Practice and Applications, (), 78-87. DOI: https://doi.org/10.54216/FPA.150107
    A., Fuqdan. Hazzaa, Firas. Sameer, Mohanad. Fadhil, Jamal. Sekhar, Ravi. Shah, Pritesh. Parihar, Sushma. Intrusion Detection in Software-Defined Networks: Leveraging Deep Reinforcement Learning with Graph Convolutional Networks for Resilient Infrastructure. Fusion: Practice and Applications , no. (2024): 78-87. DOI: https://doi.org/10.54216/FPA.150107
    A., F. , Hazzaa, F. , Sameer, M. , Fadhil, J. , Sekhar, R. , Shah, P. , Parihar, S. (2024) . Intrusion Detection in Software-Defined Networks: Leveraging Deep Reinforcement Learning with Graph Convolutional Networks for Resilient Infrastructure. Fusion: Practice and Applications , () , 78-87 . DOI: https://doi.org/10.54216/FPA.150107
    A. F. , Hazzaa F. , Sameer M. , Fadhil J. , Sekhar R. , Shah P. , Parihar S. [2024]. Intrusion Detection in Software-Defined Networks: Leveraging Deep Reinforcement Learning with Graph Convolutional Networks for Resilient Infrastructure. Fusion: Practice and Applications. (): 78-87. DOI: https://doi.org/10.54216/FPA.150107
    A., F. Hazzaa, F. Sameer, M. Fadhil, J. Sekhar, R. Shah, P. Parihar, S. "Intrusion Detection in Software-Defined Networks: Leveraging Deep Reinforcement Learning with Graph Convolutional Networks for Resilient Infrastructure," Fusion: Practice and Applications, vol. , no. , pp. 78-87, 2024. DOI: https://doi.org/10.54216/FPA.150107

    Article Statistics

    View
    55543
    Download
    890

    Download