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This study audits label provenance in encrypted traffic benchmarks, revealing how flawed labeling strategies like coarse inheritance or overstrict filtering artificially constrain model performance ceilings. By analyzing 14 benchmark entries, the authors demonstrate that discarding flows lacking explicit SNI metadata significantly degrades accuracy, while leveraging co-occurrence features from discarded connections can substantially improve macro accuracy. The findings offer critical guidance for researchers building datasets and training classifiers on encrypted traffic to ensure robustness against label noise.
SoK: Where Do Flow Labels Come From? Auditing Label Provenance in Encrypted Traffic Benchmarks
arXiv:2609.02140v1 Announce Type: cross Abstract: Encrypted traffic classification infers semantics beyond the flow record from transport-layer observables, and supervised training rests on labels that hold for the individual flow they are attached to. Recent systematizations scrutinize model in- puts and data splits; we systematize the complementary label side. Across 14 audited benchmark entries, we identify two recurring label-side strategies: coarse inheritance, which risks labelling flows the evidence does not cover, and overstrict filtering, which keeps only self-attesting flows and risks dis- carding relevant ones. No audited entry exposes a countable pre-selection population, and the task objects downstream papers attach to the same labels disagree with the recovered record in 8 of 23 referenced cells. Under strict side-channel features we derive a representation-relative ceiling on bal- anced accuracy for any classifier restricted to those features: on the public benchmarks that inherit, it ranges from 0.56 to 0.76. On the filtering side, only 24.95% of connections in our fully captured corpus carry an observable SNI of their own; yet the discarded connections raise macro accuracy from 0.44 to 0.65 through same-run co-occurrence features. We end with recommendations for benchmark builders and users.
Title:SoK: Where Do Flow Labels Come From? Auditing Label Provenance in Encrypted Traffic Benchmarks
Abstract:Encrypted traffic classification infers semantics beyond the flow record from transport-layer observables, and supervised training rests on labels that hold for the individual flow they are attached to. Recent systematizations scrutinize model in- puts and data splits; we systematize the complementary label side. Across 14 audited benchmark entries, we identify two recurring label-side strategies: coarse inheritance, which risks labelling flows the evidence does not cover, and overstrict filtering, which keeps only self-attesting flows and risks dis- carding relevant ones. No audited entry exposes a countable pre-selection population, and the task objects downstream papers attach to the same labels disagree with the recovered record in 8 of 23 referenced cells. Under strict side-channel features we derive a representation-relative ceiling on bal- anced accuracy for any classifier restricted to those features: on the public benchmarks that inherit, it ranges from 0.56 to 0.76. On the filtering side, only 24.95% of connections in our fully captured corpus carry an observable SNI of their own; yet the discarded connections raise macro accuracy from 0.44 to 0.65 through same-run co-occurrence features. We end with recommendations for benchmark builders and users.
Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG)
arXiv:2609.02140 [cs.NI]
(or arXiv:2609.02140v1 [cs.NI] for this version)
https://doi.org/10.48550/arXiv.2609.02140 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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