Papers for

internet privacy tool developers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

FlowAtom improves multi-label website identification from encrypted traffic

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 https://github.com/aimafan123/FlowAtom.

Thu 24 SeptMachine LearningNetworking and Internet Architecture
The gist
It’s hard to figure out which websites someone visited when their internet traffic is encrypted and mixed together. The authors propose FlowAtom, a way to combine small parts of internet data, called Atoms, from different pieces of traffic to recognize sets of visited websites better. They train FlowAtom without knowing website labels ahead of time and show it works well on different encrypted traffic types. Their approach beats other methods in tests where multiple monitored websites appear together.
Open → 2609.29330v1