iProfile: Collecting and Analyzing Keystroke Dynamics from Android Users
Bibliographic record
Abstract
Keystroke dynamics is one of the most popular behavioural biometrics that are currently being used as a second factor of authentication for many web services and applications.One of the reasons that makes it really popular is that it is a resettable biometric, which meets one of the main usability requirements of authentication systems.With the recent advances in mobile technologies, developers and researchers utilized several machine learning algorithms to identify smartphone users based on their keystroke dynamics.The biggest problem that faces researchers in this area is the ability to collect datasets from smartphone users that could be used to train the machine learning algorithms and, hence, create accurate predictive model.This paper introduces iProfile, a native Android application that collects keystroke dynamics from Android smartphone users.This application opens the door for researchers to recruit participants from all over the world to contribute to the data collection of keystroke dynamics.Our iProfile application allows researchers to study the impact of several parameters, such as hardware brands, users' geolocation, native language text direction, and several other factors, on the accuracy of machine learning classifiers.It also helps maintain a standard benchmark for keystroke dynamics.Having a standard benchmark helps researchers better evaluate their work based on consistent data collection procedures and evaluation metrics.This paper explains the main building blocks of the iProfile application, the algorithms used in the implementation, the communication protocol with the database server, the structure and format of the generated dataset and the feature extraction approaches.As a proof of concept, the app was used to develop a novel feature-set that identifies Android users based on 147 features.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".