ELEMTC:An Efficient Multi-Task Model with Pre-trained Transformer for Encrypted Traffic Classification

Accurate classification of encrypted traffic is vital for effective network management and Quality of Service (QoS). Protocol identification and application recognition are central to this process. Existing solutions generally use separate models for protocol identification and application recognition. However, running these models in parallel in online environments significantly increases system complexity, resource consumption, and maintenance costs. Additionally, these models often suffer from poor traffic representation by discarding crucial byte information and retaining irrelevant biases. This paper introduces an efficient multi-task learning framework, ELEMTC, designed to simultaneously perform protocol identification and application recognition, while offering a tailored traffic representation scheme for network management. To improve efficiency, we propose a novel sample pre-training task, Detection Token Replacement, specifically tailored for the networking domain. Experimental results on the ISCX VPN-non VPN dataset show that ELEMTC achieves Fl scores of 98.37% and 99.46% for application recognition and protocol identification, respectively. These results demonstrate that ELEMTC significantly outperforms current state-of-the-art methods. Furthermore, ELEMTC demonstrates outstanding efficiency by improving inference speed 20 times while maintaining relatively low memory usage.