Sep 20, 2017 · The first step is extracting feature set from raw network capture by using open source NetMake tool [3]. For accurate labeling purposes, only ...
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In this study, we propose a method for automatic extraction of features from raw network capture and accurate identification of network applications by applying ...
This study proposes a method for automatic extraction of features from raw network capture and accurate identification of network applications by applying ...
In this paper, we proposed three approaches to identify encrypted traffic and classify different applications such as browsing, VOIP, file transfer and video ...
May 29, 2024 · Our review paper encompasses studies pertaining to the detection of encrypted harmful traffic while also emphasizing platforms beyond mobile ...
Apr 10, 2023 · Our model can identify most categories of network traffic including encrypted and malicious traffic data. The experimental results show that the average ...
Recently, the usage of machine learning (ML) models in malware detection and traffic classification has been explored [9, 17, 19, 23]. These ML models, such as ...
In this paper, we formulate a universal framework of machine learning based encrypted malicious traffic detection techniques and provided a systematic review.
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How can malware be detected in encrypted traffic?
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Can IDS detect encrypted traffic?
In this context, Machine Learning (ML) approaches have shown promise in this area especially for detecting and classifying the encrypted traffic data.
Oct 27, 2023 · Machine learning-based traffic identification technology can successfully detect traffic anomalies on a network, thus revealing unknown network ...