Version 1
: Received: 18 April 2023 / Approved: 18 April 2023 / Online: 18 April 2023 (10:41:32 CEST)
How to cite:
Zhou, Y.; Han, X.; Liu, L.; Dong, Z. Machine Learning-Based Classification of Digital Media Traffic. Preprints2023, 2023040518. https://doi.org/10.20944/preprints202304.0518.v1
Zhou, Y.; Han, X.; Liu, L.; Dong, Z. Machine Learning-Based Classification of Digital Media Traffic. Preprints 2023, 2023040518. https://doi.org/10.20944/preprints202304.0518.v1
Zhou, Y.; Han, X.; Liu, L.; Dong, Z. Machine Learning-Based Classification of Digital Media Traffic. Preprints2023, 2023040518. https://doi.org/10.20944/preprints202304.0518.v1
APA Style
Zhou, Y., Han, X., Liu, L., & Dong, Z. (2023). Machine Learning-Based Classification of Digital Media Traffic. Preprints. https://doi.org/10.20944/preprints202304.0518.v1
Chicago/Turabian Style
Zhou, Y., Ling-hui Liu and Zhi-han Dong. 2023 "Machine Learning-Based Classification of Digital Media Traffic" Preprints. https://doi.org/10.20944/preprints202304.0518.v1
Abstract
The exponential growth of digital media content has introduced new challenges in managing and classifying internet traffic. Digital media traffic is composed of various applications such as video, audio, social media, and search, and its data structure is complex, incorporating a vast array of features. The classification of traffic data is a crucial aspect of internet traffic management and network security, and it forms the basis for several scenarios, including content distribution, advertising recommendations, and data analysis. Traditional classification methods rely mainly on deep packet inspection and port-based techniques, which have become increasingly ineffective due to the rapid evolution of network traffic. To address this issue, this study proposes a machine learning-based traffic classification method aimed at enhancing the accuracy and efficiency of digital media traffic classification to meet the current needs of traffic management and network security. The paper also analyzes and evaluates the classification effect and prediction capability of various algorithms under different training set sizes to validate the feasibility and effectiveness of the proposed method. The result demonstrates that the neural network algorithm has superior classification and prediction capabilities compared to the decision tree and support vector machine algorithms. Furthermore, our proposed method achieves the highest accuracy of 96.88% with a large training sample of 40,000 data streams, proving its superiority in handling high-dimensional data and complex datasets. The research results are significant for the development of digital media traffic classification and prediction methods and are expected to be applied in practical scenarios.
Keywords
Digital media; Traffic classification; Machine learning; Decision trees; Support vector machines; Neural networks.
Subject
Computer Science and Mathematics, Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
