Developing machine learning based framework for the network traffic prediction Machine learning based Traffic Prediction Section Original Research

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Published Oct 31, 2022
https://doi.org/10.47164/ijngc.v13i3.787
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Volume 13, Special Issue 3, October 2022

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Murugesan G
Department of Computer Science and Engineering, St. Joseph's College of Engineering, Chennai
Rachana Jaiswal
Department of Business Management, School of Management, HNB Garhwal (A Central) University, Srinagar, Uttarakhand, India
Sapna Singh Kshatri
Shri Shankaracharya Institute Of Professional Management And Technology, Raipur, India
Devanand Bhonsle
The Shri Shankaracharya Engineering College, Junwani, Chhattisgarh, India

Abstract

Network traffic analysis is a crucial step in developing efficient congestion control systems and identifying valid and malicious packets. Because network resources are apportioned based on predicted usage, these solutions reduce network congestion. For a variety of reasons, including dynamic bandwidth allocation, network security, and network planning, the ability to forecast network traffic is critical. Machine learning (ML) techniques to network traffic analysis have received a lot of interest. This article outlines an approach for analyzing network traffic. Three machine learning-based methodologies make up the methodology. The experimental investigation employed the NSL KDD data set. On the basis of accuracy and other criteria, KNN, Support vector machine, and nave bayes are compared.

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
G, M., Jaiswal, R. ., Singh Kshatri, S. ., & Bhonsle, D. . (2022). Developing machine learning based framework for the network traffic prediction: Machine learning based Traffic Prediction. International Journal of Next-Generation Computing, 13(3). https://doi.org/10.47164/ijngc.v13i3.787
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