IP-TRAFFIC CLASSIFICATION MODEL BASED ON MACHINE LEARNING WAYS

Authors

Madjit Malikovich Karimov, State Test Center under the Cabinet of Ministers of the Republic of Uzbekistan, Address: Bogishamol street, 12, 100084, Tashkent city, Republic of UzbekistanFollow
Sherzod Rajaboevich Gulomov, Department of Providing Information Security, Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Address: Amir Temur street, 108, 100200, Tashkent city, Republic of UzbekistanFollow

Abstract

This article analyzes machine learning ways for classifying traffic filtering: supervised learning and unsupervised learning; semi-supervised learning; reinforcement learning- classification and their use for segmentation and recognition of objects. A method of teaching with supervised learning for filtering traffic is applied, and a classifier training model with supervised learning is proposed.

First Page

123

Last Page

128

References

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3. Gulomov Sherzod Rajaboevich, Xoshimova Charos Saidaminovna, Ganiyeva Toxira Irkinovna, Djurayeva Shoxista Tagirovna, “Analysis of Methods for Measuring Available Bandwidth and Classification of Network Traffic”, International Journal of Emerging Trends in Engineering Research, vol. 8, no. 6, June 2020. pp. 2753-2759.

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Recommended Citation

Karimov, Madjit Malikovich and Gulomov, Sherzod Rajaboevich (2020) "IP-TRAFFIC CLASSIFICATION MODEL BASED ON MACHINE LEARNING WAYS," Chemical Technology, Control and Management: Vol. 2020: Iss. 5, Article 22.
DOI: https://doi.org/10.34920/2020.5-6.123-127