A Comparative Analysis of Machine Learning Models for Behavioral Biometric Authentication using Keystroke Dynamics
Bibliographic record
Abstract
Behavioral Biometrics provides a secure method to authenticate users in computer systems. Keystroke dynamics offers a promising approach in behavioral biometrics for user authentication in computer systems because users exhibit distinctive characteristics during typing. This study uses timing data from the Carnegie Mellon University (CMU) benchmark dataset to systematically evaluate the performance of a diverse set of machine learning models in classifying users based on their keystroke behavior. The machine learning models include traditional algorithms such as Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), and advanced gradient boosting techniques like XGBoost, LightGBM, and deep learning architectures, specifically Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). Our results demonstrate that the LightGBM model achieves the highest accuracy of 94.68%, significantly outperforming prior hybrid approaches like the POHMM/SVM Model (86.8%). These findings contribute valuable insights for the future development of authentication applications using behavioral biometrics.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".