Traffic Classification using Deep Learning Approach for End-to-End Slice Management in 5G/B5G

Mohammedali, N A, Kanakis, Triantafyllos, Al-Sherbaz, Ali ORCID: 0000-0002-0995-1262 and Agyeman, Michael Opoku (2022) Traffic Classification using Deep Learning Approach for End-to-End Slice Management in 5G/B5G. In: International Conference on ICT Convergence. International Conference on Information and Communication Technology Convergence (ICTC), 2022 . IEEE, 357 -362. ISBN 9781665499392

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11731 Mohammedali, Kanakis, Al-Sherbaz and Agyeman (2022) Traffic_Classification_using_Deep_Learning_Approach_for_End_to_End_Slice_Management_in_5G_B5G.pdf - Accepted Version
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Abstract

Network slicing is a key role in future networks.5G networks are intended to meet different service demands of an application offered to users. 5G architecture is used to match the requirement of the Quality of Service (QoS) by addressing different scenarios in terms of latency, scalability and throughput with different service types. Using machine learning with network slicing allows network operators to create multiple virtual networks or slices on the same physical infrastructure. These slices are independent and customized. Precisely, These slices will be managed dynamically according to the requirements defined between the network operators and the users. For this research, multi-machine learning algorithms are used to train our model, classify network traffic and predict accurate slice type for each user. After the traffic classification, we compared and analysed the performance of various machine learning algorithms in terms of learning percentage, accuracy, precision and F1 score.

Item Type: Book Section
Article Type: Article
Uncontrolled Keywords: 5G; Machine Learning; Network Slicing; Services; NFV; SDN; Deep Learning; End-to-End; Classification Mode
Related URLs:
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Schools and Research Institutes > School of Business
Research Priority Areas: Applied Business & Technology
Depositing User: Kate Greenaway
Date Deposited: 09 Nov 2022 16:04
Last Modified: 31 Oct 2023 12:19
URI: https://eprints.glos.ac.uk/id/eprint/11731

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