One Train for Two Tasks: An Encrypted Traffic Classification Framework Using Supervised Contrastive Learning

• URL: http://arxiv.org/abs/2402.07501v1
• Date: Mon, 12 Feb 2024 09:10:09 GMT
• Title: One Train for Two Tasks: An Encrypted Traffic Classification Framework Using Supervised Contrastive Learning
• Authors: Haozhen Zhang, Xi Xiao, Le Yu, Qing Li, Zhen Ling, Ye Zhang
• Abstract summary: We propose an effective model named a Contrastive Learning Enhanced Temporal Fusion (CLE-TFE) In particular, we utilize supervised contrastive learning to enhance the packet-level and flow-level representations. 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.
• Score: 18.63871240173137
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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