UDDA-TC: Unsupervised Real-Time Drift Detection and Adaptation for Continual Traffic Classification in Mobile Edge Computing

The proliferation of consumer electronics and 5G advancements have driven explosive growth in network traffic. Classifying traffic on mobile edge devices is critical for consumer electronics services. While deep learning-based Traffic Classification (TC) methods show promise, the dynamic and heterogeneous nature of edge networks causes network traffic Data Distribution Drift (DDD) and further Continual Learning (CL), degrading TC performance. Although various supervised drift detection methods have been explored, most inevitably highly depend on data labeling. Additionally, unsupervised methods incur high resource consumption and false positive rate. To address this issues, we propose an Unsupervised Real-Time Drift Detection and Adaptation Method for Continual Traffic Classification (UDDA-TC). Firstly, we propose a sliding window-based adaptive weighted Cumulative Sum (CUSUM) detector for stable drift monitoring and efficient DDD detection. Building on this, we introduce a Weighted Logits Variation (WLV)-based unsupervised real-time drift detection method enhances detection sensitivity without labels. Finally, to integrate CL into drift adaptation on edge devices, we propose a training set reconstruction strategy combines adaptive multi-window sampling with representative historical data retention to minimize labeling efforts, storage costs, and privacy risks for edge deployment. Experiments on public and private network traffic datasets showed that UDDA-TC outperforms other methods across metrics.