Affiliation:
1. Department of Computer Science and Engineering National Institute of Textile Engineering and Research (NITER), Constituent Institute of Dhaka University Dhaka Bangladesh
2. Department of Computer Science and Engineering Mawlana Bhashani Science and Technology University Dhaka Bangladesh
3. Department of Electrical Engineering and Information Technologies University of Napoli “Federico II” Napoli Italy
4. Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh
5. Department of Computer Science and Engineering Jashore University of Science and Technology Jashore Bangladesh
Abstract
AbstractThe fifth generation (5G) of mobile communications is the most exciting emerging technology for researchers and scientists to get the full benefit of a network system. However, 5G networks confront massive threats and vulnerabilities including protection, privacy, and secrecy. To face these challenges in the increasingly interconnected Internet of Things (IoT) scenario, we aim to leverage state‐of‐the‐art technologies as software defined networking (SDN) in conjunction with network function virtualization (NFV), blockchain, and machine learning (ML). Indeed, these technologies convey a robust and secure setting in the networking platform enabling to manage several criticalities related to security, privacy, flexibility, and performance. In light of these considerations, in this article, we propose the “BlockSD‐5GNet” architecture to efficiently improve the security of a 5G network and to exploit the combined advantages of Blockchain, SDN, NFV, and ML. In the proposed architecture, the SDN helps to manage the network by dividing it into data plane and control plane, while the Blockchain guarantees improved security and confidentiality. Therefore, the “BlockSD‐5GNet” architecture can both secure sensitive data and attain reliable data transfer within and between the 5G network‐infrastructure planes. Additionally, an ML module is integrated into the SDN controller to estimate network bandwidth and assist the administrator in taking effective decisions and satisfying high‐bandwidth demand. We assess the performance of the “BlockSD‐5GNet” architecture via an experimental evaluation performed in a simulation environment, and show the effectiveness of the proposed solution in comparison with baseline schemes. Finally, we also demonstrate the capability of different ML models in bandwidth prediction.
Cited by
3 articles.
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