Traffic Classification Using Machine Learning Models in Electromagnetic Nano-Networks

DOI

10.2991/978-94-6463-471-6_113How to use a DOI?

Keywords

Nano-Networks; Nano-Sensors; Supervised Machine Learning Algorithms; Port-based technique; Load-based technique

Abstract

The proliferation of Nano-sensors linked to wireless electromagnetic Nano-networks has raised the volume of traffic in numerous ways, but it has also opened up a lot of new opportunities for the Internet of Nano-things. When a nano-network is linked to the Internet by micro or nano gateways, it becomes more difficult to evaluate its general operation and classify the various flows that take place inside. Machine learning has been shown to be the most promising method, while port-based analysis and load-based analysis have also proved beneficial in the past. Finding the best model to analyse the massive amounts of data generated by real-world Nano-networks is difficult because machine learning algorithms have such a profound effect on traffic classification and overall network performance evaluation.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Subba Rao Polamuri
AU  - V. S. Naıdu
AU  - D. V. Reddy
AU  - D. H. Sudha
AU  - B. Suphanı
AU  - K. V. N. Kumar
PY  - 2024
DA  - 2024/07/30
TI  - Traffic Classification Using Machine Learning Models in Electromagnetic Nano-Networks
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 1182
EP  - 1188
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_113
DO  - 10.2991/978-94-6463-471-6_113
ID  - Polamuri2024
ER  -