QUIC Network Traffic Classification Using Ensemble Machine Learning Techniques

The Quick UDP Internet Connections (QUIC) protocol provides advantages over traditional TCP, but its encryption functionality reduces the visibility for operators into network traffic. Many studies deploy machine learning and deep learning algorithms on QUIC traffic classification. However, standalo...

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Vydané v:Applied sciences Ročník 13; číslo 8; s. 4725
Hlavní autori: Almuhammadi, Sultan, Alnajim, Abdullatif, Ayub, Mohammed
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.04.2023
Predmet:
Accuracy
Analysis
Classification
Data mining
Deep learning
ensemble learning
Internet
Investment analysis
Machine learning
Malware
Methods
QUIC traffic
Social networks
traffic classification
Transmission Control Protocol/Internet Protocol (Computer network protocol)
ISSN:2076-3417, 2076-3417
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Popis
Shrnutí:The Quick UDP Internet Connections (QUIC) protocol provides advantages over traditional TCP, but its encryption functionality reduces the visibility for operators into network traffic. Many studies deploy machine learning and deep learning algorithms on QUIC traffic classification. However, standalone machine learning models are subject to overfitting and poor predictability in complex network traffic environments. Deep learning on the other hand requires a huge dataset and intensive parameter fine-tuning. On the contrary, ensemble techniques provide reliability, better prediction, and robustness of the trained model, thereby reducing the chance of overfitting. In this paper, we approach the QUIC network traffic classification problem by utilizing five different ensemble machine learning techniques, namely: Random Forest, Extra Trees, Gradient Boosting Tree, Extreme Gradient Boosting Tree, and Light Gradient Boosting Model. We used the publicly available dataset with five different services such as Google Drive, YouTube, Google Docs, Google Search, and Google Music. The models were trained using a different number of features on different scenarios and evaluated using several performance metrics. The results show that Extreme Gradient Boosting Tree and Light Gradient Boosting Model outperform the other models and achieve one of the highest results among the state-of-the-art models found in the literature with a simpler model and features.
Bibliografia:ObjectType-Article-1
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ISSN:2076-3417
2076-3417
DOI:10.3390/app13084725