AI RESEARCH
UniAlign: A Model-Agnostic Framework for Robust Network Traffic Classification under Distribution Shifts
arXiv CS.LG
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ArXi:2605.17575v1 Announce Type: new Network traffic classification (NTC) models often suffer severe performance degradation when deployed in real-world environments due to distribution shifts caused by changing network conditions. Existing robustness-enhancing approaches are commonly coupled to specific model architectures or data settings, fail to generalize to state-of-the-art raw-byte-based NTC models, or incur significant