Unlocking Few-Shot Encrypted Traffic Classification: A Contrastive-Driven Meta-Learning Approach
Zheng Li, Jian Wang, Yafei Song, Shaohua Yue · Electronics · 2025
The classification of encrypted traffic is critical for network security, yet it faces a significant “few-shot” challenge as novel applications with scarce labeled data continuously emerge. This complexity arises from the high-dimensional, noisy nature of traffic data, making it difficult for models to generalize from few examples. Existing paradigms, such as meta-learning from scratch or standard pre-train/fine-tune methods, often fail in this scenario. To address this gap, we propose Contrastive Learning Meta-Flow (CL-MetaFlow), a novel two-stage learning framework that uniquely synergizes the strengths of contrastive representation learning and meta-learning adaptation. In the first stage, a robust feature encoder is pre-trained using supervised contrastive learning on known traffic classes, shaping a highly discriminative and metric-friendly embedding space. In the second stage, this pre-trained encoder initializes a Prototypical Network, enabling rapid and effective adaptation to new, unseen classes from only a few samples. Extensive experiments on a benchmark dataset (ISCX-VPN-2016 & ISCX-Tor-2017) demonstrate the superiority of our approach. Notably, in a five-way five-shot setting, CL-MetaFlow achieves a Macro F1-Score of 0.620, significantly outperforming from-scratch ProtoNet (0.384), a standard fine-tuning baseline (0.160), and strong pre-training counterparts like SimCLR+ProtoNet (0.545) and a re-implemented T-Sanitation (0.591). Our work validates that a high-quality, domain-adapted feature prior is the key to unlocking high-performance few-shot learning in complex network environments, providing a practical and powerful solution for real-world traffic analysis.