Multicriteria Classification Method to Evaluate the Security of Information Systems for Emerging Enterprises

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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Publication Details

Journal
Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l Administration
Published
2025-07-05
DOI
https://doi.org/10.1002/cjas.70019
Primary Topic
Information and Cyber Security
Type
article
Field-Weighted Citation Impact
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article

Multicriteria Classification Method to Evaluate the Security of Information Systems for Emerging Enterprises

Inès Saad, Wafa Bouaynaya
Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l Administration
Information and Cyber Security
article

Multicriteria Classification Method to Evaluate the Security of Information Systems for Emerging Enterprises

Inès Saad, Wafa Bouaynaya
article en

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.

Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l AdministrationVol. 43(1)
UniLaSalle Amiens (ESIEE-Amiens) (FR), Excelia Business School (FR), Université de Picardie Jules Verne (FR)
Openalex Percentile: Top 76%
Information and Cyber Security
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