A Short Review on the Dynamic Slice Management in Software-Defined Network Virtualization

Hassan, Mohamed Khalafalla; Sayed Ariffin, Sharifah Hafizah; Syed-Yusof, Sharifah Kamila; Ghazali, Nurzal Effiyana; Obeng, Kobby Asare

doi:10.48084/etasr.6394

Cited by 2 publications

(3 citation statements)

References 27 publications

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“…Conflicts may occur in the controller and flow table depending on changes made to the flow rule policies or entries. It is essential to identify priorities and take action to create SDN rules and flow entries [10,22]. Traditional networks and SDNs are widely recognized to differ significantly in features, particularly priority and action [21].…”

Section: Problem Definitionmentioning

confidence: 99%

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Utilizing Extremely Fast Decision Tree (EFDT) Algorithm to Categorize Conflict Flow on a Software-Defined Network (SDN) Controller

Khairi,

Ali Abdalla,

Hassan

et al. 2024

Eng. Technol. Appl. Sci. Res.

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Software-Defined Networks (SDNs) provide a contemporary approach to networking technology, offering a versatile and dynamically efficient network architecture for enhanced surveillance and performance. However, SDN architectures may encounter flow conflicts. These conflicts arise when modifications are made to specific flow properties, such as priority, match field, and action. Despite the existence of recommended solutions, the process of resolving conflicts in SDN continues to encounter difficulties. This study proposes an Extremely Fast Decision Tree (EFDT) classification technique to detect and categorize conflicts inside the flow table. The novelty of this method is based on the development of an accurate and effective machine-learning technique implemented on the Ryu controller plane and validated using the Mininet simulator. The effectiveness and efficiency of the proposed method were evaluated using various indicators, demonstrating superior performance in recognizing and categorizing conflict flow types in all flow sizes ranging from 10,000 to 100,000.

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Section: Problem Definitionmentioning

confidence: 99%

“…The controller's activities influence the success of an SDN. An OpenFlow switch has multiple flow tables linked to the controller via the OpenFlow protocol to distribute, classify, and assign packets based on flow entries [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Utilizing Extremely Fast Decision Tree (EFDT) Algorithm to Categorize Conflict Flow on a Software-Defined Network (SDN) Controller

Khairi,

Ali Abdalla,

Hassan

et al. 2024

Eng. Technol. Appl. Sci. Res.

Self Cite

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“…NFV allows the virtualization of network functions, enabling flexible and rapid deployment of security services. SDN, on the other hand, separates the control plane from the data plane, providing a centralized view of the network that can be crucial for security monitoring and policy enforcement [13]. The utilization of SDN in network slicing represents a paradigm shift in network security, offering a more agile and responsive framework.…”

Section: ) Virtualization-based Security Approachesmentioning

confidence: 99%

G-GANS for Adaptive Learning in Dynamic Network Slices

Alanazi

2024

Eng. Technol. Appl. Sci. Res.

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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.

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A Short Review on the Dynamic Slice Management in Software-Defined Network Virtualization

Cited by 2 publications

References 27 publications

Utilizing Extremely Fast Decision Tree (EFDT) Algorithm to Categorize Conflict Flow on a Software-Defined Network (SDN) Controller

Utilizing Extremely Fast Decision Tree (EFDT) Algorithm to Categorize Conflict Flow on a Software-Defined Network (SDN) Controller

G-GANS for Adaptive Learning in Dynamic Network Slices

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