A Comparative Study on Online Machine Learning Techniques for Network Traffic Streams Analysis

Authors

  • D. Haripriya Associate Professor, Department of CSE, Veltech Ranagarajan Dr.Saguntala R&D Institute of Science and Technology, Avadi,Chennai,600062, Tamilnadu
  • Mahmoud Abou Ghaly Assistant Professor, Department of Mathematics, Faculty of Science, Ain Shams University, Cairo, Egypt
  • A. Deepak Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamilnadu
  • Kamal Sharma Department of Mechanical Engineering, GLA University, Mathura
  • Shanker Chandre Assistant Professor, Department of Computer Science & Artificial intelligence, SR University, Warangal, Telangana
  • K. K. Bajaj RNB Global University, Bikaner
  • Anurag Shrivastava Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamilnadu

Keywords:

Machine learning, Online learning, Network traffic streams, Network traffic classification, Internet of Things, Deep Learning

Abstract

Modern networks are responsible for the generation of massive volumes of traffic and data streams. This data analysis is essential for a wide range of activities, including the management of network resources and the investigation of issues about cyber security. It is of the utmost importance to develop methods of data analysis that are able to assess network data in real time and deliver results that are dependent on the acquisition of new data. It is anticipated that approaches for online machine learning (OL) will make these types of data analytics viable. As part of this study, we investigate and compare a number of OL strategies that provide data stream analytics for the networking industry. During the course of our research into the benefits of traffic data analytics, we focused not only on the benefits of online learning in this field but also on its shortcomings, such as concept drift and uneven classes. We also investigate whether or not these frameworks and tools are compatible with the data processing frameworks that are already in use. These many frameworks and technologies each come with their own individual sets of benefits and drawbacks. In order to assess the effectiveness of OL methods, we conduct an empirical inquiry on the performance of a variety of ensemble- and tree-based network traffic categorization algorithms. At the conclusion, there is a discussion of the issues that have not been satisfactorily answered as well as possible directions for the future of traffic data stream analysis. In the field of networking, addressing the goals and objectives of online data streams analytics and learning was the purpose of the study that was conducted.

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References

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Published

29.01.2024

How to Cite

Haripriya, D. ., Ghaly, M. A. ., Deepak, A. ., Sharma, K. ., Chandre, S. ., Bajaj, K. K. ., & Shrivastava, A. . (2024). A Comparative Study on Online Machine Learning Techniques for Network Traffic Streams Analysis. International Journal of Intelligent Systems and Applications in Engineering, 12(13s), 09–19. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4562

Issue

Vol. 12 No. 13s (2024)

Section

Research Article

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