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DQSA: Dynamic Quantized Self-Attention for Multi-Task Encrypted Network Traffic Classification
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AI
DOI：10.1109/TIFS.2025.3647211.png)
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Abstract
En 中文
Network traffic classification is crucial for both network security and management. Despite advances in deep learning-based multi-task traffic classification, existing models often struggle to jointly handle multiple tasks while providing interpretable insights. In multi-task scenarios, different tasks rely on distinct regions of the traffic sequence, motivating the use of dynamic and interpretable attention mechanisms. To this end, we propose Dynamic Quantized Self-Attention (DQSA), a unified framework specifically designed for multi-task network traffic classification. At its core, the Task Gated Attention Router (TGAR) dynamically associates attention heads with different tasks, enabling adaptive focus on task-specific patterns. This mechanism provides interpretable attention scores, which help analyze misclassifications and guide further model refinement. To improve efficiency and handle diverse network traffic features, we introduce the Soft Quantized Self-Attention Head (SQ-SAH) to reduce computational complexity and extend the Rotary Position Embedding (RoPE) to accommodate these features. Extensive experiments on ISCX VPN-nonVPN and DCI-LTE datasets demonstrate that DQSA consistently outperforms state-of-the-art baselines, achieving 92.85% accuracy on the encapsulation-level task of ISCX VPN-nonVPN and 93.17% accuracy on the application-level task of DCI-LTE, surpassing the strongest existing methods by up to 2.65%, while providing interpretable task-specific attention for efficient multi-task network traffic classification.
Keywords:
Traffic classification
self-attention mechanism
multi-task learning
interpretability
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