ANASETC: Automatic Neural Architecture Search for Encrypted Traffic Classification

Abstract

The widespread adoption of encrypted network protocols has made traffic encryption ubiquitous, creating substantial challenges for network management and security. This paper introduces a novel encrypted traffic classification system, ANASETC, which combines traffic burst features with Neural Architecture Search (NAS) to automatically design efficient neural network architectures. ANASETC autonomously generates high-performance classification models, significantly reducing manual intervention while maintaining high classification accuracy. To enhance search efficiency, we introduce a new search space called ETNasnet, which optimizes the training process through parameter sharing among sub-models. We evaluate ANASETC’s performance on three public datasets and a real-world satellite network traffic dataset. The results show that ANASETC achieves an optimal balance between classification accuracy and search efficiency, demonstrating strong robustness and adaptability across various task scenarios, outperforming state-of-the-art methods.