PIRAT - Tool for Automated Cyber-risk Assessment of PLC Components & Systems Deploying NVD CVE & MITRE ATT&CK Databases
Bibliographic record
Abstract
Programmable Logic Controllers (PLCs) are the backbone of modern-day Industrial Control Systems (ICSs), and as such play a key role in many critical infrastructure sectors (e.g., water and water-waste management, power distribution, transportation, food and agriculture, critical manufacturing, etc.). Given the important functions that PLCs carry out within many critical infrastructures, a cyber-compromise of even a single PLC device can have far-reaching impact and consequences, ranging from distribution-system outages, environmental pollution, mass water and food poisoning, to outright loss of human life. The objective of this work-in-progress is to develop a free open source tool, named PIRAT, for cyber-risk assessment of individual PLC components, as well as more complex PLC systems. The tool synthesizes the user-provided PLC component/system information with the readily available data from the National Vulnerability Database (NVD) and MITRE Adversarial Tactics, Techniques and Common Knowledge (MITRE ATT&CK) database. The output of the tool is an aggregate risk scores for the given PLC component/system. The risk score is derived not only based on the known PLC vulnerabilities, but also based on the presence and capabilities of advance persistent threat (APT) groups potentially targeting the given PLC component/system and/or targeting the respective critical infrastructure industry.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.014 |
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".