Chengxiang Chang

Encrypted traffic classification is a fundamental task in cybersecurity and network traffic management. With the popularity of deep learning, many studies have used it to achieve high accuracy in this field. However, these models rely on large amounts of labeled data and require retraining or fine-tuning when faced with new tasks, which is time-consuming and resource-intensive. Due to this limitation, few-shot traffic classification methods have emerged to reduce dependence on large labeled datasets by using only a few samples. However, these models often still require pre-training and fine-tuning to perform well. Meanwhile, Large Language Models (LLMs), such as ChatGPT, have attracted attention for their capacity to learn new concepts during inference without fine-tuning. Inspired by this, we propose In-Context Encrypted Traffic, a novel few-shot network traffic classification method allowing for few-shot classification without the need for fine-tuning. We use two existing encrypted traffic datasets to demonstrate the effectiveness of our method when facing new samples. We evaluate our method on a realistic 5-way few-shot classification task over encrypted traffic, and it achieves up to 5.7% higher accuracy than representative meta-learning baselines, without requiring fine-tuning.