# QUIC Network Traffic Classification Using Ensemble Machine Learning Techniques
###### Abstract  
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. QUIC traffic; traffic classification; machine learning; ensemble learning +
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apps that are used by the public such as Instagram and Facebook [6]. Additionally, the main objective to develop QUIC was to overcome the latency problems of other protocols [3]. This indicates that deeper analysis might be needed to test the security of that protocol. The ability to reveal the service producing some traffic may threaten one of the main security goals, which is confidentiality. For that purpose, we aim to test how confidential QUIC is against machine learning techniques to identify secure traffic. In the past, network traffic classification techniques were applied based on the TCP/UDP port numbers. However, the QUIC protocol is more complex than the traditional network protocols, which means the classical techniques may not be efficient for classifying traffic that uses this protocol. Machine learning (ML) and deep learning (DL) techniques have proven their reliability and robustness in many real-world problems. Therefore, it is efficient to utilize such techniques in the QUIC traffic classification. However, one problem with classical ML is that they are subjected to over-fitting. On the other hand, DL techniques require a large volume of data to produce better results. For that purpose, a better approach is to train models that are more robust than classical ML and do not require the same amount of data required as DL, such as ensemble machine learning techniques. In this paper, we examine the QUIC traffic classification using five ensemble machine learning techniques, namely: Random Forest (RF), Extra Trees (ET), Gradient Boosting Tree (GBT), Extreme Gradient Boosting Tree (XGBT), and Light Gradient Boosting Model (LGBM). We use a publicly available dataset to experiment and validate our ensemble models using four evaluation metrics: precision, recall, F1-score, and accuracy. The models were trained using 15, 30, 40, 50, 60, 80, 100, and 120 packets as features. In addition to that, the models were trained and tested using two different training and testing scenarios, i.e., holdout and cross-validation. Moreover, a comparative analysis of the models is conducted. According to our exhaustive and extensive literature search and review, the study of QUIC traffic classification using machine learning is very limited. Especially, award-winning XGBT and LGBM are not explored in QUIC traffic classification.