# Fast and Accurate Multi-Task Learning for Encrypted Network Traffic Classification
## 2 Related Works
### Encrypted Traffic Classification  
Network traffic classification has been around for a long time and has primarily utilized traditional methods based on port and payload, as well as signature-based methods. However, traditional traffic analysis methods are ineffective because many modern applications, including mobile, cloud, and IoT, rely primarily on encrypted traffic. To address the limitations of traditional methods, recent research has turned to learning-based approaches involving ML and DL [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25]. In [10], [PERSON] et al. introduced Deep Packet, a system that utilizes a stacked autoencoder and CNN. They achieved an impressive F1 score of 98% for application identification on the ISCX 2016 VPN/Non-VPN dataset. [PERSON] et al. [11] introduced a novel method to convert packets into images and process them using 1D-CNN, which showed promising results on ISCX 2016 VPN/Non-VPN. In [12], [PERSON] et al. pre-sent an encrypted network traffic classification approach using CNNs and LSTM networks; in [13], they proposed an innovative fusion of CNNs and designed RNNs for service recognition in IoT traffic; in [14], they used naive Bayes, C4. 5 decision trees, Bayesian networks, and naive Bayes trees. They performed a comprehensive analysis comparing the performance of these algorithms using 22 features extracted from network flows. In [15], they introduced flow sequence network (FS-NET) for encrypted traffic classification. FS-NET utilizes both RNNs and a multi-layer encoder-decoder structure. In [16], the authors proposed FlowPic, a classification method that converts consecutive packet sizes in a flow into a two-dimensional gray image and uses CNNs for classification. While FlowPic is simple and performs well, it is not suitable for real-time traffic classification because it requires the capture of traffic over a long period of time. The authors also note that it is not applicable to classifying some encrypted traffic. In [17], the authors proposed TSCRNN, which automatically extracts features for efficient traffic classification based on spatiotemporal features. To validate the proposed method, the authors conducted experiments on ISCX Tor 2016 data and obtained high accuracy. In [18], the authors proposed MIMETIC, which exploits traffic data heterogeneity by learning both intra- and inter-modality dependencies to overcome performance limitations. MIMETIC outperforms single-modality DL-based, state-of-the-art ML-based mobile traffic classifiers. In [19], the authors propose an improved DAGSVM classification method by focusing on the error accumulation of the traditional DAGSVM algorithm. Experimental results show that the proposed method has higher classification accuracy than traditional DAGSVM while having an acceptable time cost. The studies in [39] and [47] have conducted research with a focus on lightweight models rather than classification performance. While most studies primarily emphasize performance, they highlight the importance of lightweight approaches for handling large-scale traffic data. In recent years, there has been a surge in research centered on transformer architectures characterized by self-attention and multi-headed attention mechanisms. Transformer-structured models mainly utilize the BERT model, which has proven to show strong performance in the NLP field, but recently, research has also been conducted using the masked autoencoder (MAE), which is used in the CV field [33; 34; 35]. In [34], the authors proposed ET-BERT, a novel approach inspired by transformer architectures. It presents a new pre-training method designed for encrypted traffic classification and fine-tuned for optimal performance achieving an accuracy of over 97%. In [21], the authors propose a method called PERT (payload encoding representation from transformer) utilizing dynamic word embedding. PERT outperforms other methodologies on publicly available encrypted traffic datasets and captures Android HTTPS traffic.