Analyzing Data Granularity Levels for Insider Threat Detection Using Machine Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Malicious insider attacks represent one of the most damaging threats to networked systems of companies and government agencies. There is a unique set of challenges that come with insider threat detection in terms of hugely unbalanced data, limited ground truth, as well as behaviour drifts and shifts. This work proposes and evaluates a machine learning based system for user-centered insider threat detection. Using machine learning, analysis of data is performed on multiple levels of granularity under realistic conditions for identifying not only malicious behaviours, but also malicious insiders. Detailed analysis of popular insider threat scenarios with different performance measures are presented to facilitate the realistic estimation of system performance. Evaluation results show that the machine learning based detection system can learn from limited ground truth and detect new malicious insiders in unseen data with a high accuracy. Specifically, up to 85% of malicious insiders are detected at only 0.78% false positive rate. The system is also able to quickly detect the malicious behaviours, as low as 14 minutes after the first malicious action. Comprehensive result reporting allows the system to provide valuable insights to analysts in investigating insider threat cases.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it