Multicriteria Classification Method to Evaluate the Security of Information Systems for Emerging Enterprises
Bibliographic record
Abstract
ABSTRACT This paper presents a method for evaluating the security level of information systems in emerging enterprises by identifying relevant assessment criteria and constructing a preference model adapted to their organizational context. The method is based on the dominance‐based rough set approach (DRSA), which generates interpretable classification rules from qualitative assignment examples. An empirical study was conducted with 34 emerging enterprises in the Hauts‐de‐France region of France. The proposed method consists of two phases. In Phase 1, decision rules were inferred from 19 assignment examples. In Phase 2, these rules were applied to classify 15 additional enterprises according to their security level from low to high. The approach combines supervised learning with multicriteria decision modelling. The preference model (decision rules) constructed from the learning set was found to be understandable and applicable by the decision‐makers we interviewed, which supports transparency and trust in the classification results.
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How this classification was reachedexpand
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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".