ETKD: A Semi-Supervised Learning-based Knowledge Distillation Model for Encrypted Traffic Classification

Encrypted traffic classification is a challenging task involving precisely categorizing various types of network traffic and applications, a challenge exacerbated by the continuous emergence of new applications. Traditional methods predominantly rely on feature extraction from datasets and the utilization of complex deep learning models for classification, necessitating larger datasets and more intricate computational models. The challenge lies in balancing leveraging unlabeled traffic to enhance classification datasets and controlling model complexity. In response to these challenges, this paper introduces ETKD, an encrypted traffic classification framework based on knowledge distillation. ETKD is designed to address the trade-off between classification accuracy and model complexity. The framework capitalizes on image recognition techniques to extract encrypted traffic features from extensive unlabeled traffic data. Subsequently, these features are fine-tuned using a teacher-student model, achieving a harmonious equilibrium between reducing model complexity and maintaining high classification accuracy. The experimental results presented in this paper provide compelling evidence of the framework's effectiveness. Compared to state-of-the-art models, the minimal model employed in our approach achieves notably higher accuracy and F1-score, underscoring the practicality and efficiency of the proposed ETKD method.