DEMUX achieves state-of-the-art multi-tab website fingerprinting accuracy by preserving boundary signals, modeling at multiple scales, and associating dispersed traffic fragments with a new three-component architecture.
Robust multi-tab website fingerprinting attacks in the wild,
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
UniAlign improves robustness of deep learning NTC models under distribution shifts via domain alignment fine-tuning and stable ensembling, yielding 2.51% accuracy and 2.71% F1 gains over standard training on three public datasets.
citing papers explorer
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DEMUX: Boundary-Aware Multi-Scale Traffic Demixing for Multi-Tab Website Fingerprinting
DEMUX achieves state-of-the-art multi-tab website fingerprinting accuracy by preserving boundary signals, modeling at multiple scales, and associating dispersed traffic fragments with a new three-component architecture.
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UniAlign: A Model-Agnostic Framework for Robust Network Traffic Classification under Distribution Shifts
UniAlign improves robustness of deep learning NTC models under distribution shifts via domain alignment fine-tuning and stable ensembling, yielding 2.51% accuracy and 2.71% F1 gains over standard training on three public datasets.