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.

Leveraging the advanced capabilities of Deep Neural Networks (DNNs), attackers can precisely detect users' online activities through traffic monitoring, nullifying the efficacy of current encrypted communication tools/protocols and progressively resulting in privacy leakage. Several defensive methods against DNN-based traffic monitoring (DTM) have been proposed; however, these methods often rely excessively on prior knowledge and incur inevitable additional bandwidth overhead (BWO). Moreover, they frequently generate invalid packets that violate network transmission constraints. To address these drawbacks, in this paper, we propose BYTEFLIPPING, a byte-space grey-box defensive method, which perturbs vulnerable bytes in the transport layer payload to generate adversarial sample packets. We design a Payload Byte Vulnerability Ranking algorithm to pinpoint the most vulnerable bytes and based on this generate adversarial packets to defend DTM. Extensive experiments reveal that ByteFLIPPING performs well in protecting against three DTM methods across two benchmark datasets, significantly decreasing the accuracy of the state-of-the-art ET-BERT by 94%. Compared to baseline defensive methods, BYTEFLIPPING incurs no extra BWO, offers more dependable packet validity, and boasts greater feasibility.