Enhancing Security Anomaly Alert Prioritization through Calibrated Standard Deviation Uncertainty Estimation with an Ensemble of Auto-Encoders
Bibliographic record
Abstract
Deep auto-encoders (AEs) are widely employed deep learning methods in the field of anomaly detection, especially detection of security malicious activities. Cybersecurity analysts face challenges in managing a large volume of alerts, often constrained by limited processing resources. In response, various strategies, including false positive reduction, human-in-the-loop and alert prioritization, are employed. This paper explores the integration of uncertainty quantification (UQ) methods into alert prioritization for anomaly detection using an ensemble of AEs. UQ models recognize doubtful classification decisions, aiding analysts in treating the most certain alerts first (as a more certain alert is more likely to be accurate). Our study reveals a nuanced issue where applying UQ to an ensemble of AEs can lead to skewed distributions of large reconstruction errors, falsely indicating large standard deviation uncertainties on certain classification decisions. Contrary to intuition, AEs suggest that large reconstruction errors could indicate small uncertainties. To address this, we propose an extension for obtaining a calibrated standard deviation uncertainty distribution, mitigating erroneous alert prioritization. Evaluation on 6 benchmark intrusion detection datasets demonstrates that our proposed calibration approach enhances UQ methods' ability to prioritize alerts with a favorable trade-off in other invaluable performance metrics.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".