FOADA: Toward Robust Open-World Mobile App Fingerprinting
Bibliographic record
Abstract
Smartphone users are susceptible to a privacy leakage attack called App Fingerprinting (AF), where traffic analysis is used to infer the apps in use. Despite packet encryption, AF attacks leverage packet size and timing information to identify apps, posing a privacy threat. However, existing attacks fail when a few apps are used concurrently, causing unsegmented traffic with app multiplexing and overlapping. The key reason is that they cannot accurately identify active time boundaries for the apps. This paper presents a novel AF attack, FOADA, the first to accurately predict both the location and label of a target app in traffic. FOADA approaches AF as an object detection problem, training a deep learning model to estimate boundary positions and classify traffic segments. Accurate boundary predictions help the model focus on the most relevant traffic segment, enhancing its classification performance. FOADA excels in handling noisy app traffic. With app multiplexing, it achieves an F1-score of 0.96 for predicting only app labels and an F1-score of 0.92 for predicting both app labels and their locations. FOADA surpasses the state-of-the-art attack PacketPrint, which achieves F1-scores of 0.80 and 0.48 in these two scenarios, respectively. The inference time of FOADA is 2,000 times faster than PacketPrint.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".