Transfer Learning for Behavioral Biometrics-based Continuous User Authentication
Bibliographic record
Abstract
The cybersecurity industry is developing innovative solutions to avoid cyber-attacks. One such upcoming technology is continuous user authentication. It uses keystrokes and mouse movement behavioral patterns to authenticate the user continuously in the background. This technique uses machine learning to classify users based on the behavioral pattern. It requires a lot of data to find the user’s behavioral pattern and plenty of time is required to gather the data which extends the start of continuously authenticating the new user. In this research, the transfer learning technique was used for a feed-forward neural network model to overcome this issue for new users. Experiments were done using only one behavioral pattern with a set of 5 users to find the difference in accuracy between the model trained with transfer learning and the model trained without any previous learning. The results showed that the model using transfer learning had 9.76% more accuracy than the model trained from scratch. This implies that using transfer learning improves the accuracy with a small amount of data which will help to speed up the onboarding process for new users. This work generates new knowledge which will allow the researchers to implement various machine learning techniques with multiple behavioral patterns, thereby providing the best model performance for transfer learning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".