FlowAtom: Atom-Based Evidence Aggregation for Multi-Label Website Fingerprinting
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Computer Science > Machine Learning
Title:FlowAtom: Atom-Based Evidence Aggregation for Multi-Label Website Fingerprinting
Abstract:Identifying the set of monitored websites in mixed encrypted traffic is challenging because an individual flow often provides only partial evidence of website identity. To address this challenge, we propose FlowAtom, which constructs shared prototypes, called Atoms, from flow representations without website labels. Specifically, FlowAtom pretrains a flow encoder on external unlabeled traffic and aggregates Atom responses across flows within each observation window into a fixed-dimensional, permutation-invariant representation for monitored website-set prediction. Across Direct HTTPS, Trojan, and VMess, FlowAtom achieves micro-F1 scores of 97.82%, 94.43%, and 93.92% in closed-world evaluation, respectively, and consistently outperforms the evaluated baselines in open-world evaluation on windows containing monitored visits. The code is available at this https URL.
| Comments: | 5 pages. Submitted to ICASSP 2027 |
| Subjects: | Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI) |
| Cite as: | arXiv:2609.29330 [cs.LG] |
| (or arXiv:2609.29330v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.29330
arXiv-issued DOI via DataCite (pending registration)
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