Explicit Behavioral Embedding Method for Generating Explanations of Insider Threat Events
Bibliographic record
Abstract
Insider threats pose significant risks to organizational security; however, many detection models lack transparency, which hinders their practical deployment due to high false-positive rates. We propose a novel framework that integrates behavioral embeddings with SHAP (Shapley Additive Explanations) to enhance the explainability of unsupervised anomaly detection models. By modeling temporal and semantic aspects of user actions, our approach generates interpretable visualizations of anomalous behaviors. Applied to the CERT v4.2 Insider Threat Dataset, the framework effectively identifies and explains anomalies, such as unauthorized data transfers and malicious system activities, through clustering and feature-importance analysis. Our results demonstrate that explainable models help reduce analyst workload by filtering false positives and providing actionable insights. This approach enhances decision-making, facilitates compliance with transparency regulations such as GDPR, and addresses the trade-off between detection accuracy and interpretability, making it a valuable tool for real-world insider threat detection.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".