One Train for Two Tasks: An Encrypted Traffic Classification Framework
  Using Supervised Contrastive Learning
Haozhen Zhang Xi Xiao Le Yu Qing Li Zhen Ling Ye Zhang
As network security receives widespread attention, encrypted traffic
classification has become the current research focus. However, existing methods
conduct traffic classification without sufficiently considering the common
characteristics between data samples, leading to suboptimal performance.
Moreover, they train the packet-level and flow-level classification tasks
independently, which is redundant because the packet representations learned in
the packet-level task can be exploited by the flow-level task. Therefore, in
this paper, we propose an effective model named a Contrastive Learning Enhanced
Temporal Fusion Encoder (CLE-TFE). In particular, we utilize supervised
contrastive learning to enhance the packet-level and flow-level representations
and perform graph data augmentation on the byte-level traffic graph so that the
fine-grained semantic-invariant characteristics between bytes can be captured
through contrastive learning. We also propose cross-level multi-task learning,
which simultaneously accomplishes the packet-level and flow-level
classification tasks in the same model with one training. Further experiments
show that CLE-TFE achieves the best overall performance on the two tasks, while
its computational overhead (i.e., floating point operations, FLOPs) is only
about 1/14 of the pre-trained model (e.g., ET-BERT). We release the code at
https://github.com/ViktorAxelsen/CLE-TFE