M-ETC: Improving Multi-Task Encrypted Traffic Classification by Reducing Inter-Task Interference

With the rapid evolution of deep learning (DL), its integration in encrypted traffic classification (ETC) can automatically extract key features from raw traffic data, enhancing classification performance. So far, researchers have proposed many DL-based models for ETC. However, the complexity and dynamism of network applications lead to the diversification of ETC tasks. Current models, mostly tailored for single tasks, overlook real-world multi-tasking needs of network devices. Deploying task-specific complex models concurrently on resource-limited devices is impractical. In response to the increasing number of tasks, researchers have introduced multi-task learning frameworks for ETC, demonstrating its potential as a promising technical approach. However, current research overlooks the interference between tasks, resulting in flawed models when it comes to sharing parameters, setting learning rates, and determining loss values. Aiming at these deficiencies, we propose $\mathcal{M}$-ETC, a multi-task ETC method reducing inter-task interference. The innovation of $\mathcal{M}$-ETC lies in two aspects. Firstly, we design a hierarchical multi-task learning model (HMLM) to provide effective features for each task and prevent the impact of invalid features. Secondly, we propose a learning rate balancing strategy (LRB) for modules and a dynamic weight average strategy (DWA) for tasks’ loss values. During model training, LRB prevents overfitting and underfitting of tasks, while DWA prevents bias towards tasks with large loss values. To validate $\mathcal{M}$-ETC, we carry out comparative experiments using four encrypted traffic datasets. The experimental results show that the classification performance of $\mathcal{M}$-ETC on multiple tasks exceeds those of five state-of-the-art methods.