Evaluation of Intrusion Detection Systems in Cyber Security using Fuzzy OffLogic and MCDM Approach
Modern cybersecurity infrastructures rely heavily on Intrusion Detection Systems (IDS) to detect and prevent malicious activities and unauthorized access. Given the growing complexity of network topologies and the rising frequency of cyber threats, evaluating IDS solutions requires a systematic and...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
University of New Mexico
2025-06-01
|
| Series: | Neutrosophic Sets and Systems |
| Subjects: | |
| Online Access: | https://fs.unm.edu/NSS/20OffLogic.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849232920691081216 |
|---|---|
| author | Zhengrui Yang |
| author_facet | Zhengrui Yang |
| author_sort | Zhengrui Yang |
| collection | DOAJ |
| description | Modern cybersecurity infrastructures rely heavily on Intrusion Detection Systems (IDS) to detect and prevent malicious activities and unauthorized access. Given the growing complexity of network topologies and the rising frequency of cyber threats, evaluating IDS solutions requires a systematic and unbiased approach. In this study, thirteen widely used IDS models are assessed using a multi-criteria evaluation framework across four key dimensions: detection accuracy, resource efficiency, scalability, and false positive rate. The goal is to support informed, datadriven decision-making for stakeholders such as policymakers, IT administrators, and security analysts when selecting an appropriate IDS. The VIKOR method is employed to rank the IDS alternatives based on the assigned weights, while Fuzzy OffLogic is applied to integrate expert assessments expressed as intervals. The results reveal that modern AI-based IDS models demonstrate strong performance in scalability and resource utilization, and they outperform traditional systems in adaptability and detection accuracy. |
| format | Article |
| id | doaj-art-b2d2a24519bb4867ac19327db6038b40 |
| institution | Kabale University |
| issn | 2331-6055 2331-608X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | University of New Mexico |
| record_format | Article |
| series | Neutrosophic Sets and Systems |
| spelling | doaj-art-b2d2a24519bb4867ac19327db6038b402025-08-20T16:34:40ZengUniversity of New MexicoNeutrosophic Sets and Systems2331-60552331-608X2025-06-018534336010.5281/zenodo.15265874Evaluation of Intrusion Detection Systems in Cyber Security using Fuzzy OffLogic and MCDM ApproachZhengrui YangModern cybersecurity infrastructures rely heavily on Intrusion Detection Systems (IDS) to detect and prevent malicious activities and unauthorized access. Given the growing complexity of network topologies and the rising frequency of cyber threats, evaluating IDS solutions requires a systematic and unbiased approach. In this study, thirteen widely used IDS models are assessed using a multi-criteria evaluation framework across four key dimensions: detection accuracy, resource efficiency, scalability, and false positive rate. The goal is to support informed, datadriven decision-making for stakeholders such as policymakers, IT administrators, and security analysts when selecting an appropriate IDS. The VIKOR method is employed to rank the IDS alternatives based on the assigned weights, while Fuzzy OffLogic is applied to integrate expert assessments expressed as intervals. The results reveal that modern AI-based IDS models demonstrate strong performance in scalability and resource utilization, and they outperform traditional systems in adaptability and detection accuracy. https://fs.unm.edu/NSS/20OffLogic.pdffuzzy offlogicsecuritycyber-securityattacksintrusion detection systemmcdm approachminimum spanning treeneutrosophic graphneutrosophic numberprim’s algorithmscore function |
| spellingShingle | Zhengrui Yang Evaluation of Intrusion Detection Systems in Cyber Security using Fuzzy OffLogic and MCDM Approach Neutrosophic Sets and Systems fuzzy offlogic security cyber-security attacks intrusion detection system mcdm approach minimum spanning tree neutrosophic graph neutrosophic number prim’s algorithm score function |
| title | Evaluation of Intrusion Detection Systems in Cyber Security using Fuzzy OffLogic and MCDM Approach |
| title_full | Evaluation of Intrusion Detection Systems in Cyber Security using Fuzzy OffLogic and MCDM Approach |
| title_fullStr | Evaluation of Intrusion Detection Systems in Cyber Security using Fuzzy OffLogic and MCDM Approach |
| title_full_unstemmed | Evaluation of Intrusion Detection Systems in Cyber Security using Fuzzy OffLogic and MCDM Approach |
| title_short | Evaluation of Intrusion Detection Systems in Cyber Security using Fuzzy OffLogic and MCDM Approach |
| title_sort | evaluation of intrusion detection systems in cyber security using fuzzy offlogic and mcdm approach |
| topic | fuzzy offlogic security cyber-security attacks intrusion detection system mcdm approach minimum spanning tree neutrosophic graph neutrosophic number prim’s algorithm score function |
| url | https://fs.unm.edu/NSS/20OffLogic.pdf |
| work_keys_str_mv | AT zhengruiyang evaluationofintrusiondetectionsystemsincybersecurityusingfuzzyofflogicandmcdmapproach |