UniAlign: A Model-Agnostic Framework for Robust Network Traffic Classification under Distribution Shifts

In three linesUniAlign is a model-agnostic framework improving robustness of network traffic classification under distribution shifts. It combines domain alignment fine-tuning and stable model ensembling, achieving 2.51% accuracy and 2.71% F1 improvements on three public datasets, requiring only 12.4–53.9% of baseline training time.

Summary generated by Claude — human-verified