Human Trait Analysis via Machine Learning Techniques for User Authentication
Bibliographic record
Abstract
Machine learning is an extremely important technique that has become heavily used in different types of applications such as detection systems for fraud, intrusion or fault and monitoring systems for health or computer. Human trait analysis and identification is a field of research that needs a strong implementation for machine learning. Human trait analysis provides a tool with which human identification factors can be verified. Currently, detail aspects of human behavior are digitally and continuously logged in Big Data based platforms such as Twitter and Facebook. This continuous flow of high-volume data requires sophisticated data analysis to examine huge amounts of behavioral evidence so that human traits can be modeled. This paper proposes an innovative technique for human trait analysis that fits the needs for user's identity verification. The pioneering work of this technique is in the distinction of the normal and abnormal actions of the users, where the focus is given to these abnormal actions to establish security potential profiles. The data analysis and prediction of the proposed technique is based on the concept of machine learning and uses several models based on four techniques; K-means, Hidden Markov Model (HMM), Auto-Encoder Neural Network, and Gaussian Distribution. Experiments have been carried out in four main phases: prediction of rare user actions, filter security potential actions, build/update a user profile, and generate a real-time (i.e. just in time) set of challenging questions. Real-world scenarios are considered to demonstrate the benefits of these challenging questions in building secure knowledge-based user authentication systems.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".