Automated offense Prioritization for SIEM using ProbabilisticMachine Learning Models
Bibliographic record
Abstract
Security Information and Event Management (SIEM) systems play a crucial role in cybersecurity by collecting, analyzing, and categorizing various events to detect and mitigate potential threats. These events are processed into offenses and then presented to Security Operations Center (SOC) analysts for further investigation and response. However, manual prioritization of offenses by SOC analysts can be challenging because of resource constraints and increasing volume of events. In our proposed solution for automating offense prioritization, we utilized the prediction probability and impact score derived from probabilistic machine learning (ML) models to address this challenge. The prediction probability indicates the likelihood of the occurrence of the offense, whereas the impact score signifies the severity of the potential consequences if the offense occurs. Additionally, we integrate time-based metrics such as Mean Time to Detect (MTTD) and Mean Time to Resolve (MTTR) into our offense prioritization framework. This combination of prediction probability, impact score, MTTD, and MTTR enables SOC analysts to prioritize offenses effectively, focusing their attention on those with high likelihood, high impact, and optimized response timelines. By addressing highly likely and high-impact offenses with efficient response processes, SOC analysts can optimize their response efforts, ensure the timely detection, and mitigate genuine security threats.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".