Anomaly Detection for Insider Threats Using Unsupervised Ensembles
Bibliographic record
Abstract
Insider threat represents a major cybersecurity challenge to companies, organizations, and government agencies. Insider threat detection involves many challenges, including unbalanced data, limited ground truth, and possible user behavior changes. This research presents an unsupervised learning based anomaly detection approach for insider threat detection. We employ four unsupervised learning methods with different working principles, and explore various representations of data with temporal information. Furthermore, different computational intelligence schemes are explored to combine these models to create anomaly detection ensembles for improving the detection performance. Evaluation results show that the approach allows learning from unlabelled data under challenging conditions for insider threat detection. Insider threats are detected with high detection and low false positive rates. For example, 60% of malicious insiders are detected under 0.1% investigation budget, and all malicious insiders are detected at less than 5% investigation budget. Furthermore, we explore the ability of the proposed approach to generalize for detecting new anomalous behaviors in different datasets, i.e., robustness. Finally, results demonstrate that a voting-based ensemble of anomaly detection can be used to improve detection performance as well as the robustness. Comparisons with the state-of-the-art confirm the effectiveness of the proposed approach.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".