Empirical Risk Analysis Methodology for Adversarial Threats against Critical Infrastructure
Bibliographic record
Abstract
The increase in foreign and domestic threats mandates a serious reevaluation of existing security methodologies, standards, and vulnerability assessments. A comprehensive defense strategy with quantitative and qualitative measurements is presented on how the water sector can optimize the application and placement of physical security countermeasures to improve resilience based on known parameters in a cost effective way. This study reviews the history and original intent of these methodologies that were adopted from the atomic and nuclear segments of the energy sector. These methodologies served as a starting point for the risk assessment documents that govern water sector security. The current American National Standards Institute (ANSI) risk models used by the water sector, based on design basis threat (DBT) and risk analysis and management for critical asset protection (RAMCAP), are rooted in the traditional risk formula of threat multiplied by vulnerability multiplied by consequence. This paper concludes that due to the inability to define who the adversary is, along with their objectives, motives, and capabilities, and the lack of statistically valid datasets or available intelligence of malevolent threats, the requirements listed in these methodologies are not achievable and will remain as unknowns in water/wastewater/stormwater systems. Therefore, the risk models used for mitigating adversarial threats have fundamental errors that should be replaced by an alternate risk model capable of measuring what can be known about facility resilience to malevolent attacks. By treating risk as a vector quantity consisting of known parameters, the probability of success of a given threat can be calculated using the mathematical analysis of defense strategy and countermeasures (MADSC) methodology. Once these parameters are established, the MADSC methodology can be used to determine the degree of difficulty in compromising existing countermeasures and provide guidance for physical security improvements and budgeting based on quantitative results.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".