# QUIC Network Traffic Classification Using Ensemble Machine Learning Techniques
## 3 Related Work  
In this section, the literature related to the QUIC network traffic classification is discussed. One such research is the one conducted by [PERSON] et al. [13], where a two-stages traffic classification technique based on random forests and convolutional neural network (CNN) was proposed. Experiments on different QUIC-based services showed that the proposed technique achieves accuracy as high as 99%. [PERSON] and [PERSON] [8] proposed a multi-task traffic classification technique based on CNN that predicts the bandwidth requirements, flow duration, and traffic class. The proposed framework outperformed both single-task and transfer learning approaches on ISCX and QUIC public datasets, achieving accuracy as high as 90%. The same authors proposed a semi-supervised method based on CNN [5]. The experimental results showed that the technique is promising even when it is trained on a small dataset (20 samples per class). Another method was proposed by [PERSON] and [PERSON] [4] for intrusion detection based on a fingerprinting method using Random Forests, Decision Trees, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), AdaBoost, and Multi-layer Perceptron (MLP). Comparative analysis showed that KNN outperforms the others in all the evaluation metrics achieving a score of 99.56%. On the other hand, [PERSON] et al. in [9] proposed a feature engineering approach based on Long-Short term memory (LSTM) and  
Figure 1: QUIC connection: In 1-RTT, the user sends an empty hello message and waits for the server’s rejection message. Then, the user sends the actual hello message to establish the connection. In 0-RTT, the client establishes the connection by sending the actual hello directly, without the empty hello message. CNN that is generalizable to encrypted data web protocols. The proposed model was evaluated over QUIC traffic, where it achieved more than 99% accuracy. Another work was proposed by [PERSON] et al. [7]. The authors developed SVM, KNN, Random Forest, and neural network models for the task of encrypted traffic classification. The proposed models were tested on QUIC traffic where they showed promising results, as they all achieved more than 97% accuracy. [PERSON] and [PERSON] [10] proposed a self-supervised approach for encrypted network traffic classification with few labeled data. The proposed deep learning model achieved 98% on the QUIC dataset in terms of accuracy, outperforming the baseline by 3%. Other literature is also found in the domain, which targeted the network traffic classification but is not specific to QUIC traffic. For instance, Deep Packet [14] is a network classification framework proposed based on CNN and stacked autoencoder (SAE). The proposed model outperformed other state-of-the-art models on the \"ISCX VPN-nonVPN\". [PERSON] et al. [15] studied the impact of feature selection in the traffic classification task and conducted a comparative study on C4.5, Bayes Network, Naive Bayes, and Naive Bayes Trees. [PERSON] et al. [16] studied the network traffic classification on IoT devices by combining both CNN and RNN. It was interesting to observe that the model performs well even on a small subset of data (5-15 packets). [PERSON] et al. [17] proposed a method to distinguish between VPN and non-VPN encrypted traffic on the \"ISCX VPN-non-VPN\" dataset. The method combines the ant-lion meta-heuristic algorithm (ALO), the self-organizing map algorithm (SOM), and CNN for feature extraction and classification. Experimental results showed an accuracy of 98% on the test data. Similar work was proposed in [18] on the same dataset that combines data fusion methods and deep learning for traffic classification. In this work, Deep Belief Network (DBN), CNN, and MLP were employed to classify network traffic. Then, Bayesian decision fusion was used for the final results. The proposed method achieved an accuracy of 97%. [PERSON] et al. [19] approached the network traffic classification problem by utilizing deep learning in the detection process.