A Fuzzy Multi-Criteria Decision Framework for Selecting Cybersecurity Platforms Under Strategic PESTEL Factors
Source abstract
Abstract
The growth of advanced cyber threats has inspired organisations to start using powerful cybersecurity platforms, but the process of selection is analytically challenging due to the multidimensional, uncertain, and conflicting character of the evaluation criteria. The prevailing culture of decision-support frameworks is based on unyielding numerical evaluations that cannot reflect the underlying vagueness of expert judgment and the dynamic interplay of macro-environmental factors. This paper presents a combined Fuzzy Multi-Criteria Decision-Making (FMCDM) system, which uses polygonal fuzzy numbers, in particular pentagonal fuzzy representation, and four other complementary methods of MCDM (Fuzzy AHP, Fuzzy TOPSIS, Fuzzy VIKOR, and Fuzzy COPRAS), integrated by a Borda Count consensus system. Sixteen assessment sub-criteria are logically obtained through an analysis of PESTEL (Political, Economic, Social, Technological, Environmental, and Legal) and weighted using the Fuzzy Analytic Hierarchy Process. The model is used to compare six cybersecurity platforms, including Microsoft Security Framework, CrowdStrike Falcon, Cisco Cybersecurity Portfolio, Palo Alto Networks Cortex, Fortinet Security Fabric, and Sophos Central. In this study, Fuzzy AHP demonstrates that the aggregate weight of political factors is the highest (0.4181), followed by cross-border data management, regulatory compliance, and government incentives as the most popular sub-criteria. According to the results from the Fuzzy TOPSIS, Fuzzy VIKOR, and Fuzzy COPRAS methods, Microsoft Security Framework ranks consistently in the first place, and CrowdStrike Falcon and Cisco Cybersecurity Portfolio were ranked second and third, respectively. The framework presented in the study provides decision-makers with a reproducible, uncertainty-conscious basis for cybersecurity platform selection.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Information and Cyber Security · Infrastructure Resilience and Vulnerability Analysis · Network Security and Intrusion Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Industrial Engineering, Durban University of Technology, Durban 4000, South Africa
Institute of Systems Science, Durban University of Technology, Durban 4000, South Africa
Department of Mechanical Engineering, Bells University of Technology, Ota 112104, Ogun State, Nigeria
Ritchie School of Engineering and Computer Science, University of Denver, 2155 E Wesley Ave, Denver, CO 80210, USA
Department of Computer Science, Kwara State Polytechnic, P.M.B 1375, Ilorin 241105, Kwara State, Nigeria
References (48)
Show source-provided references
1. The economics of information security investment10.1145/581271.581274 ↗
2. The global cybercrime industry and its structure: Relevant actors, motivations, threats and countermeasures
3. European Parliament (2016). Regulation
4. (EU) 2016/679 of the European Parliament and of the Council (General Data Protection Regulation). Off. J. Eur. Union, 119, 1-88.
5. Torbacki, W. (2021). A hybrid MCDM model combining DANP and PROMETHEE II methods for the assessment of cybersecurity in industry 4.0. Sustainability, 13.10.3390/su13168833 ↗
6. Alharbi, A., Seh, A.H., Alosaimi, W., Alyami, H., Agrawal, A., Kumar, R., and Khan, R.A. (2021). Analyzing the impact of cyber security related attributes for intrusion detection systems. Sustainability, 13.10.3390/su132212337 ↗
7. An overview and multicriteria analysis of communication technologies for smart grid applications10.1016/j.prime.2023.100121 ↗
8. Babatunde, O., Emezirinwune, M., Adebisi, J., Abdulsalam, K.A., Akintayo, B., and Adebisi, J. (2024). A fuzzy multi-criteria approach for selecting sustainable power systems simulation software in undergraduate education. Sustainability, 16.10.3390/su16208994 ↗
9. Neutrosophic MCDM methodology for assessment risks of cyber security in power management10.61356/j.nswa.2023.18 ↗
10. Assessing cybersecurity risks and prioritizing top strategies in Vietnam’s finance and banking system using strategic decision-making models-based neutrosophic sets and Z number10.1016/j.heliyon.2024.e37893 ↗
11. Evaluation of intrusion detection systems in cyber security using fuzzy OffLogic and MCDM approach
12. Taborda Blandon, G.E., Hurtado Rivera, J.F., Durán Vásquez, J.M., Monsalve Ruiz, M.J., Silva Castillo, M.T., and Vargas Montoya, H.F. (2026). Selecting a cybersecurity risk analysis methodology for MSMEs using a multi-criteria method (AHP). Technologies, 14.10.3390/technologies14040227 ↗
13. Ansari, M., Ali, S.A., Alam, M., Al Hudaify, S., and Al Natour, M. (2026). Multi-criteria decision modeling for cybersecurity investment and IT risk governance. EDPACS, 1–11.10.1080/07366981.2026.2654640 ↗
14. Pentagonal fuzzy number, its properties and application in fuzzy equation10.1016/j.fcij.2017.09.001 ↗
15. Some notes on pentagonal fuzzy numbers10.22457/ijfma.v13n2a2 ↗
16. Strategy selection against cyber and phishing attacks in the financial sector using MCDM methods10.17671/gazibtd.1724633 ↗
17. Bhol, S.G. (2025). Applications of multi criteria decision making methods in cyber security. Cyber-Physical Systems Security, Springer.10.1007/978-981-97-5734-3_11 ↗
18. Decision making with the analytic hierarchy process
19. Triantaphyllou, E. (2000). Multi-Criteria Decision-Making Methods: A Comparative Study, Kluwer Academic Publishers.10.1007/978-1-4757-3157-6 ↗
20. Evaluating information security investments using the analytic hierarchy process10.1145/1042091.1042094 ↗
21. A fuzzy TOPSIS methodology to support outsourcing of logistics services10.1108/13598540610671743 ↗
22. A new dynamic MCDM for cyber-attack classification with a case study
23. Fuzzy multicriteria decision-making: A literature review10.1080/18756891.2015.1046325 ↗
24. Fuzzy hierarchical analysis10.1016/0165-0114(85)90090-9 ↗
25. Applications of the extent analysis method on fuzzy AHP10.1016/0377-2217(95)00300-2 ↗
26. Multi-attribute decision making: A simulation comparison of select methods10.1016/S0377-2217(97)00147-1 ↗
27. Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment10.1016/j.eswa.2005.09.040 ↗
28. Extensions of the TOPSIS for group decision-making under fuzzy environment10.1016/S0165-0114(97)00377-1 ↗
29. Ranking higher education institutions using entropy–VIKOR with generalized pentagonal intuitionistic fuzzy numbers10.67334/cds2120266 ↗
30. Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS10.1016/S0377-2217(03)00020-1 ↗
31. The new method of multicriteria complex proportional assessment of projects
32. An axiomatization of Borda’s rule10.1016/0022-0531(74)90073-8 ↗
33. Ishtiaq, A., Kubra, K.T., Uzair, A., and Ali, A. (2027). Quantifying multi-cause psychological disorder risk through an advanced mathematical model using intuitionistic pentagonal fuzzy logic. Spectr. Oper. Res., 1–21. Online first.10.31181/sor41202762 ↗
34. Alpha-cut representation used for defuzzification in rule-based systems10.1016/j.fss.2020.05.008 ↗
35. Decision making method for evaluating logistics companies based on the ordered representation of the polygonal fuzziness1
36. Kahraman, C., Cebi, S., Oztaysi, B., and Onar, S.C. (2023). Fuzzy sets and their extensions: Literature review on fuzzy extensions of AHP. Analytic Hierarchy Process with Fuzzy Sets Extensions, Springer.10.1007/978-3-031-39438-6 ↗
37. Multicriteria optimization of civil engineering systems
38. The problem of m rankings10.1214/aoms/1177732186 ↗
39. Siegel, S., and Castellan, N.J. (1988). Nonparametric Statistics for the Behavioural Sciences, McGraw-Hill. [2nd ed.].
40. A quantitative approach to content validity10.1111/j.1744-6570.1975.tb01393.x ↗
41. Recalculation of the critical values for Lawshe’s content validity ratio10.1177/0748175612440286 ↗
42. Application of integrated entropy-VIKOR method in assessment of e-government citizen/user satisfaction
43. Big data, fast data and data lake concepts10.1016/j.procs.2016.07.439 ↗
44. Srinivas, P., Lin, C.H., Kambhampati, S., and Shakarian, P. (2019). AI in Cybersecurity, Springer.
45. A model for evaluating IT security investments10.1145/1005817.1005828 ↗
46. Multiple criteria decision making (MCDM) methods in economics: An overview10.3846/20294913.2011.593291 ↗
47. Mémoire sur les élections au scrutin
48. Managing Delphi surveys using nonparametric statistical techniques10.1111/j.1540-5915.1997.tb01330.x ↗