FEMRA: Fuzzy Expert Model for Risk Assessment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Risk assessment is a major part of the ISMS Process. The Information Security Management System standards specify guidelines and a general framework for risk assessment. In many existing standards, such as NIST and ISO27001, risk assessment is described however, while these standards present some guidelines, there are no details on how to implement it in an organization. In a complex organization, risk assessment is a complicated process and involves a lot of assets. In this paper, we present the FEMRA model, which uses fuzzy expert systems to assess risk in organizations. The risk assessment varies considerably with the context, the metrics used as dependent variables, and the opinions of the persons involved. Fuzzy logic thus represents an excellent model for this application. Organizations can use FEMRA as a tool to improve the ISMS implementation. One of the interesting characteristics of FEMRA is that it can represent each risk with a numerical value. The managers can detect higher risks by comparing these values and develop a good strategy to reduce them.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it