Enhancing Flow Embedding Through Trace: A Novel Self-supervised Approach for Encrypted Traffic Classification

Traffic classification is a crucial task in network security and management. Recent research has shown the effectiveness of deep learning when applied to encrypted traffic classification. However, the reliance of deep learning models on abundant labeled and balanced data poses challenges, particularly in traffic analysis where labeling is costly and imbalanced traffic distribution is common. To tackle this challenge, researchers have proposed self-supervised representation learning. This approach aims to derive universal traffic representations from vast amounts of unlabeled data, reducing the need for extensive labeling in downstream tasks. Current representation learning methods predominantly focus on exploring flow-level information, neglecting valuable trace information crucial for effective flow representation learning. In this study, we introduce SAFE, a self-supervised learning methodology tailored for flow representation learning. SAFE specifically delves into trace-level (i.e., a mixture of correlated flows) information to enhance flow embedding. Moreover, our analysis reveals that existing encrypted traffic datasets often contain numerous invalid samples. SAFE conducts a comprehensive examination of dataset characteristics, filtering out these invalid samples, thereby advancing the field significantly. Extensive experiments demonstrate that SAFE outperforms state-of-the-art methods.