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A comprehensive survey of recent advancements in machine-learning-driven encrypted traffic analysis and classification and provides insights into current practices and future directions in
In this paper, we present a comprehensive survey on recent achievements in machine learning-powered encrypted traffic analysis. To begin with, we review the literature in this area and
The primary goals of our survey are two-fold: First, we present the overall procedure and provide a detailed explanation of utilizing machine learning in analyzing and classifying encrypted
The document titled "Encrypted Network Traffic Analysis and Classification Utilizing Machine Learning" discusses the importance of understanding and classifying encrypted network
This research has demonstrated the feasibility of using machine learning models for encrypted payload traffic classification. It has shown that various features, such as packet length,
In this section, we systematically introduce the major steps and the procedure in network traffic analysis and classification by utilizing the machine-learning method.
Machine learning has been emerging as a powerful tool to extract informative features without getting access to payload, and thus is widely employed in encrypted traffic analysis.