Real-Time Data Generation and Anomaly Detection for Security User Profiles
Bibliographic record
Abstract
Mostly, security user profiles are being generated from real-time sources of users’ data. User profiles generation process involves complicated and comprehensive data analysis mechanisms. Machine learning is, and become the most popular, technique among the researchers and the practitioners for this real-time data analysis. The scope of this paper is twofold: 1) to implement the anomaly detection module for real-time source of users’ data, and 2) to build real-time data generation engine to train and test this model and to provide the researchers an alternative or simulated methods for real-time data source. The data generation engine is powered by the Synthetic Data Vault (SDV) python library and is relaying on both the historical (past) and real-time (fresh) data. The purpose of using historical data generation is to increase the accuracy of anomaly detection model by expanding the user’s activity which will give more insights of the user behavior. The data generation model simulates the real-world data environments to animate the realtime based analytic systems. Anomaly detection model, in other words, is a real-time flagging system utilizing Kafka platform to generate security user profile. Therefore, the security user profiles are generated based on integrated and collaborated tools to enhance the performance of knowledge-based user authentication systems. Finally, this work generates new knowledge which will allow the researchers to implement and train various machine learning techniques with real-time data generation engine.
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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 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".