# Fast and Accurate Multi-Task Learning for Encrypted Network Traffic Classification
## 6 Conclusions  
Network traffic classification has been studied for a long time, and recently, a lot of research has been conducted on encrypted traffic. Most studies perform single-task classification, with DL- and transformer-based methods performing well. However, there are limitations in their efficiency and effectiveness given the increasingly diverse and complicated nature of traffic. In this paper, we proposed multitask classification by using DistilBERT. The proposed method can learn multiple tasks in one model with one training. We applied a weight adjustment to improve the performance of our proposed method. The weight adjustment consists of class weights and task weights. Class weights mitigate the problem of data imbalance, and task weights prevent biased learning due to the difference in difficulty between tasks in multi-task classification. To evaluate the proposed method, we conducted experiments in terms of accuracy and efficiency. Measured in terms of accuracy, the proposed approach achieves 96.89-99.29% accuracy on three tasks, showing higher performance compared to most existing methods. Furthermore, in terms of efficiency, it outperforms ET-BERT. While the proposed method exhibits lower efficiency compared to FastTraffic and MATEC, which focus on lightweight design, it achieves a significantly higher accuracy, ranging from 4.65 to 27.68% higher than the two mentioned methods. We discussed the performance impact of class weight and weight adjustment in Section 5. In addition, we validated the decision to select 8 packets and 63 bytes based on performance experiments with input data shapes (in Appendix A, Table 12). This input shape consists of packets generated during the handshake process within TLS, which is the most widely utilized today and typically remains unencrypted. Therefore, we believe it performs well despite being encrypted traffic. However, the proposed method has some limitations. First, although the proposed method demonstrated high performance on the ISCX 2016 VPN/Non-VPN dataset, validation was only conducted on specific datasets. As the ISCX 2016 VPN/Non-VPN dataset comprises a small amount of data, leveraging AI models may yield high performance. Therefore, additional validation experiments on other datasets such as ISCX Tor are necessary to verify the performance of the proposed method. Second, as mentioned earlier, efficiency can vary depending on hardware performance and the dataset. In this paper, we evaluated the method using results presented in other studies; however, for a precise assessment, consistent experimental conditions and preprocessed datasets are necessary. Third, as previously mentioned, the proposed method utilizes eight packets in the flow, which results in a relatively high time to process a single packet. Fourth, the proposed method applies class weights to address the problem of imbalanced data. Although the class weights alleviate the problem of imbalanced data to some extent, they are still unevenly distributed. Nevertheless, the proposed method can perform three tests with one training and shows high performance and efficiency. In future research, we plan to perform multi-task classification using diverse datasets. We will assess the effectiveness of our proposed method using identical experimental setups and preprocessed datasets for evaluation. Additionally, we plan to improve the model architecture and preprocessing methods to further enhance the performance and efficiency of the proposed method, including PT. **Author Contributions:** Conceptualization, M.-S.K. ; methodology, J.-T.P. ; software, J.-T.P. and C.-Y.S. ; resources, J.-T.P., [PERSON]-Y.S. and [PERSON]-J.B.; data processing, U.-J.B. and J.-T.P. writing--original draft preparation, J.-T.P. ; writing--review and editing, J.-T.P. ; visualization, J.-T.P. ; supervision, M.-S.K. ; project administration, M.-S.K. ; funding acquisition, M.-S.K. All authors have read and agreed to the published version of the manuscript.