Automating cyber security advisories: Supervised machine learning for automated decision making
Bibliographic record
Abstract
There is an everlasting struggle for organisations to remediate vulnerabilities in IT systems before being the victim of an exploitation. Organisations try to reduce this struggle by turning to specialized cyber organisations, which use their expertise to recommend resolving a subset of vulnerabilities. Unfortunately, the process of recommending a selection of vulnerabilities is primarily done manually. Manual labour is time consuming and requires skilled personnel. Automating cyber advisories reduces both these problems. We introduce ACSA, a process designed for the Automation of Cyber Security Advisories. ACSA creates a dataset that can be used by advisory publishers to automate their publications with minimal effort. The dataset contains around 90,000 advisories which are filtered by a machine learning model to the set published by the organisation. We applied the ACSA process and dataset to both the Dutch and Canadian NCSC and found that on average we can already automate the majority of advisories. This constitutes a significant workload reduction in comparison to the situation prior to the automation. Even better results are observed when looking at the performance of ACSA on specific vendors. For some vendors we are able to automate more than 90% of the advisories while creating minimal false positives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".