Convolutions are Competitive with Transformers for Encrypted Traffic Classification with Pre-training

• URL: http://arxiv.org/abs/2508.02001v1
• Date: Mon, 04 Aug 2025 02:42:44 GMT
• Title: Convolutions are Competitive with Transformers for Encrypted Traffic Classification with Pre-training
• Authors: Chungang Lin, Weiyao Zhang, Tianyu Zuo, Chao Zha, Yilong Jiang, Ruiqi Meng, Haitong Luo, Xuying Meng, Yujun Zhang,
• Abstract summary: We investigate whether convolutions, with linear complexity and implicit positional encoding, are competitive with Transformers in encrypted traffic classification with pre-training.<n>We propose NetConv, a novel pre-trained convolution model for encrypted traffic classification.<n>We show that NetConv improves average classification performance by 6.88% and model throughput by 7.41X over existing pre-trained models.
• Score: 12.150588010496165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Encrypted traffic classification is vital for modern network management and security. To reduce reliance on handcrafted features and labeled data, recent methods focus on learning generic representations through pre-training on large-scale unlabeled data. However, current pre-trained models face two limitations originating from the adopted Transformer architecture: (1) Limited model efficiency due to the self-attention mechanism with quadratic complexity; (2) Unstable traffic scalability to longer byte sequences, as the explicit positional encodings fail to generalize to input lengths not seen during pre-training. In this paper, we investigate whether convolutions, with linear complexity and implicit positional encoding, are competitive with Transformers in encrypted traffic classification with pre-training. We first conduct a systematic comparison, and observe that convolutions achieve higher efficiency and scalability, with lower classification performance. To address this trade-off, we propose NetConv, a novel pre-trained convolution model for encrypted traffic classification. NetConv employs stacked traffic convolution layers, which enhance the ability to capture localized byte-sequence patterns through window-wise byte scoring and sequence-wise byte gating. We design a continuous byte masking pre-training task to help NetConv learn protocol-specific patterns. Experimental results on four tasks demonstrate that NetConv improves average classification performance by 6.88% and model throughput by 7.41X over existing pre-trained models.

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