G-GANS for Adaptive Learning in Dynamic Network Slices
Received: 11 February 2024 | Revised: 24 March 2024 | Accepted: 8 April 2024 | Online: 1 June 2024
Corresponding author: Meshari Huwaytim Alanazi
Abstract
This paper introduces a novel approach to improve security in dynamic network slices for 5G networks using Graph-based Generative Adversarial Networks (G-GAN). Given the rapidly evolving and adaptable nature of 5G network slices, traditional security mechanisms often fall short in providing real-time, efficient, and scalable defense mechanisms. To address this gap, this study proposes the use of G-GAN, which combines the strengths of Generative Adversarial Networks (GANs) and Graph Neural Networks (GNNs) for adaptive learning and anomaly detection in dynamic network environments. The proposed approach utilizes GAN to generate realistic network traffic patterns, both normal and adversarial, whereas GNNs analyze these patterns within the context of the network's graph-based topology. This combination facilitates the early detection of anomalies and potential security threats, adapting to the ever-changing configurations of network slices. The current study presents a comprehensive methodology for implementing G-GAN, including system architecture, data processing, and model training. The experimental analysis demonstrates the efficacy of G-GAN in accurately identifying security threats and adapting to new scenarios, revealing that G-GAN outperformed established models with an accuracy of 97.12%, precision of 96.20%, recall of 97.24%, and F1-Score of 96.72%. This study not only contributes to the field of network security in the context of 5G, but also opens avenues for future exploration in the application of hybrid AI models for real-time security across various domains.
Keywords:
anomaly detection, generated adversarial networks, accuracy, adaptive learning, graph neural networksDownloads
References
A. A. Barakabitze, A. Ahmad, R. Mijumbi, and A. Hines, "5G network slicing using SDN and NFV: A survey of taxonomy, architectures and future challenges," Computer Networks, vol. 167, Feb. 2020, Art. no. 106984.
S. D’Oro, F. Restuccia, A. Talamonti, and T. Melodia, "The Slice Is Served: Enforcing Radio Access Network Slicing in Virtualized 5G Systems," in IEEE INFOCOM 2019 - IEEE Conference on Computer Communications, Paris, France, Apr. 2019, pp. 442–450.
A. Kaloxylos, "A Survey and an Analysis of Network Slicing in 5G Networks," IEEE Communications Standards Magazine, vol. 2, no. 1, pp. 60–65, Mar. 2018.
F. Salahdine, Q. Liu, and T. Han, "Towards Secure and Intelligent Network Slicing for 5G Networks," IEEE Open Journal of the Computer Society, vol. 3, pp. 23–38, 2022.
"Minimum requirements related to technical performance for IMT-2020 radio interface(s)," International Telecommunication Network, ITU-R M.2410-0, Nov. 2017.
"NGMN 5G White Paper," Next Generation Mobile Networks Ltd, Feb. 2015.
"Ericsson Mobility Report," Ericsson, Jun. 2020.
R. Khan, P. Kumar, D. N. K. Jayakody, and M. Liyanage, "A Survey on Security and Privacy of 5G Technologies: Potential Solutions, Recent Advancements, and Future Directions," IEEE Communications Surveys & Tutorials, vol. 22, no. 1, pp. 196–248, 2020.
M. A. Ferrag, L. Maglaras, A. Argyriou, D. Kosmanos, and H. Janicke, "Security for 4G and 5G cellular networks: A survey of existing authentication and privacy-preserving schemes," Journal of Network and Computer Applications, vol. 101, pp. 55–82, Jan. 2018.
I. Ahmad, T. Kumar, M. Liyanage, J. Okwuibe, M. Ylianttila, and A. Gurtov, "5G security: Analysis of threats and solutions," in 2017 IEEE Conference on Standards for Communications and Networking (CSCN), Helsinki, Finland, Sep. 2017, pp. 193–199.
A. A. Mir, M. F. Zuhairi, and S. Musa, "Graph Anomaly Detection with Graph Convolutional Networks," International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 11, 2023.
R. F. Olimid and G. Nencioni, "5G Network Slicing: A Security Overview," IEEE Access, vol. 8, pp. 99999–100009, 2020.
M. K. Hassan, S. H. Sayed Ariffin, S. K. Syed-Yusof, N. E. Ghazali, and K. A. Obeng, "A Short Review on the Dynamic Slice Management in Software-Defined Network Virtualization," Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12074–12079, Dec. 2023.
M. K. Hassan et al., "DLVisor: Dynamic Learning Hypervisor for Software Defined Network," IEEE Access, vol. 11, pp. 84144–84167, 2023.
G. Gardikis et al., "SHIELD: A novel NFV-based cybersecurity framework," in 2017 IEEE Conference on Network Softwarization (NetSoft), Bologna, Italy, Jul. 2017.
J. Lam and R. Abbas, "Machine Learning based Anomaly Detection for 5G Networks." arXiv, Mar. 06, 2020.
M. Mwita, J. Mbelwa, J. Agbinya, and A. E. Sam, "The Effect of Hyperparameter Optimization on the Estimation of Performance Metrics in Network Traffic Prediction using the Gradient Boosting Machine Model," Engineering, Technology & Applied Science Research, vol. 13, no. 3, pp. 10714–10720, Jun. 2023.
H. N. Dai, Z. Zheng, and Y. Zhang, "Blockchain for Internet of Things: A Survey," IEEE Internet of Things Journal, vol. 6, no. 5, pp. 8076–8094, Jul. 2019.
A. Nassar and Y. Yilmaz, "Deep Reinforcement Learning for Adaptive Network Slicing in 5G for Intelligent Vehicular Systems and Smart Cities," IEEE Internet of Things Journal, vol. 9, no. 1, pp. 222–235, Jan. 2022.
K. Abbas, M. Afaq, T. A. Khan, A. Mehmood, and W.-C. Song, "IBNSlicing: Intent-Based Network Slicing Framework for 5G Networks using Deep Learning," in 2020 21st Asia-Pacific Network Operations and Management Symposium (APNOMS), Daegu, Korea (South), Sep. 2020, pp. 19–24.
R. K. Gupta, S. Mahajan, and R. Misra, "Resource orchestration in network slicing using GAN-based distributional deep Q-network for industrial applications," The Journal of Supercomputing, vol. 79, no. 5, pp. 5109–5138, Mar. 2023.
I. Farris, T. Taleb, Y. Khettab, and J. Song, "A Survey on Emerging SDN and NFV Security Mechanisms for IoT Systems," IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp. 812–837, 2019.
C. Zhang, S. Yu, Z. Tian, and J. J. Q. Yu, "Generative Adversarial Networks: A Survey on Attack and Defense Perspective," ACM Computing Surveys, vol. 56, no. 4, Aug. 2023, Art. no. 91.
I. Goodfellow et al., "Generative Adversarial Nets," in Advances in Neural Information Processing Systems, 2014, vol. 27.
A. Creswell, T. White, V. Dumoulin, K. Arulkumaran, B. Sengupta, and A. A. Bharath, "Generative Adversarial Networks: An Overview," IEEE Signal Processing Magazine, vol. 35, no. 1, pp. 53–65, Jan. 2018.
H. Zenati, C. S. Foo, B. Lecouat, G. Manek, and V. R. Chandrasekhar, "Efficient GAN-Based Anomaly Detection." arXiv, May 01, 2019.
Z. Wang, Q. She, and T. E. Ward, "Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy," ACM Computing Surveys, vol. 54, no. 2, Oct. 2021, Art. no. 37.
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini, "The Graph Neural Network Model," IEEE Transactions on Neural Networks, vol. 20, no. 1, pp. 61–80, Jan. 2009.
W. Hamilton, Z. Ying, and J. Leskovec, "Inductive Representation Learning on Large Graphs," in Advances in Neural Information Processing Systems, 2017, vol. 30.
W. Jiang, J. Luo, M. He, and W. Gu, "Graph Neural Network for Traffic Forecasting: The Research Progress," ISPRS International Journal of Geo-Information, vol. 12, no. 3, Mar. 2023, Art. no. 100.
M. K. Hassan et al., "Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts," Sensors, vol. 22, no. 9, Jan. 2022, Art. no. 3592.
E. Caville, W. W. Lo, S. Layeghy, and M. Portmann, “Anomal-E: A self-supervised network intrusion detection system based on graph neural networks,” Knowledge-Based Systems, vol. 258, Dec. 2022, Art. no. 110030.
J. Donahue, P. Krähenbühl, and T. Darrell, "Adversarial Feature Learning." arXiv, Apr. 03, 2017.
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville, "Improved Training of Wasserstein GANs," in Advances in Neural Information Processing Systems, 2017, vol. 30.
N. Moustafa and J. Slay, "UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)," in 2015 Military Communications and Information Systems Conference (MilCIS), Canberra, Australia, Nov. 2015.
Downloads
How to Cite
License
Copyright (c) 2024 Meshari Huwaytim Alanazi
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
