Machine Learning Models with Neutrosophic Numbers for Network Anomaly Detection and Security Defense Technology
In the dynamic world of cybersecurity, strong solutions are necessary to safeguard intricate network systems. By looking at network anomaly detection and security protection, this study investigates how machine learning (ML) might increase digital infrastructure security. We assess how well critical...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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University of New Mexico
2025-05-01
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| Series: | Neutrosophic Sets and Systems |
| Subjects: | |
| Online Access: | https://fs.unm.edu/NSS/3AnomalyDetection.pdf |
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| _version_ | 1849224589917290496 |
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| author | Hussein S Al-Khazraji Ahmed M. Alkhamees Humam M Al-Doori Ahmed A. Metwaly Mohamed eassa Ahmed Abdelhafeez Ahmed S. Salama Ahmad M. Nagm |
| author_facet | Hussein S Al-Khazraji Ahmed M. Alkhamees Humam M Al-Doori Ahmed A. Metwaly Mohamed eassa Ahmed Abdelhafeez Ahmed S. Salama Ahmad M. Nagm |
| author_sort | Hussein S Al-Khazraji |
| collection | DOAJ |
| description | In the dynamic world of cybersecurity, strong solutions are necessary to safeguard intricate network systems. By looking at network anomaly detection and security protection, this study investigates how machine learning (ML) might increase digital infrastructure security. We assess how well critical ML approaches, such as ensemble approaches and supervised learning, identify anomalies and lessen risks. The examination of ML-based systems integration into comprehensive security frameworks places a strong emphasis on real-time monitoring and adaptive responses. Examples from real-world situations highlight how crucial ML is to improving network security. After, we apply different ML models to the real-world dataset. Then we use the single-valued Neutrosophic numbers (SVNNs) methodology to evaluate these ML models and select the best one. We use the multi-criteria decision-making (MCDM) approach to obtain the criteria weights and rank the ML models using the EDAS method. The results show that the random forest model is the best ML model under different evaluation matrices. |
| format | Article |
| id | doaj-art-0305a3a3b0e2413a890ab5f3e7274d46 |
| institution | Kabale University |
| issn | 2331-6055 2331-608X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | University of New Mexico |
| record_format | Article |
| series | Neutrosophic Sets and Systems |
| spelling | doaj-art-0305a3a3b0e2413a890ab5f3e7274d462025-08-25T07:42:41ZengUniversity of New MexicoNeutrosophic Sets and Systems2331-60552331-608X2025-05-0183506410.5281/zenodo.15122434Machine Learning Models with Neutrosophic Numbers for Network Anomaly Detection and Security Defense TechnologyHussein S Al-KhazrajiAhmed M. AlkhameesHumam M Al-DooriAhmed A. MetwalyMohamed eassaAhmed AbdelhafeezAhmed S. SalamaAhmad M. NagmIn the dynamic world of cybersecurity, strong solutions are necessary to safeguard intricate network systems. By looking at network anomaly detection and security protection, this study investigates how machine learning (ML) might increase digital infrastructure security. We assess how well critical ML approaches, such as ensemble approaches and supervised learning, identify anomalies and lessen risks. The examination of ML-based systems integration into comprehensive security frameworks places a strong emphasis on real-time monitoring and adaptive responses. Examples from real-world situations highlight how crucial ML is to improving network security. After, we apply different ML models to the real-world dataset. Then we use the single-valued Neutrosophic numbers (SVNNs) methodology to evaluate these ML models and select the best one. We use the multi-criteria decision-making (MCDM) approach to obtain the criteria weights and rank the ML models using the EDAS method. The results show that the random forest model is the best ML model under different evaluation matrices. https://fs.unm.edu/NSS/3AnomalyDetection.pdfneutrosophic numbersecuritynetwork anomaly detectioncybersecurityuncertainty |
| spellingShingle | Hussein S Al-Khazraji Ahmed M. Alkhamees Humam M Al-Doori Ahmed A. Metwaly Mohamed eassa Ahmed Abdelhafeez Ahmed S. Salama Ahmad M. Nagm Machine Learning Models with Neutrosophic Numbers for Network Anomaly Detection and Security Defense Technology Neutrosophic Sets and Systems neutrosophic number security network anomaly detection cybersecurity uncertainty |
| title | Machine Learning Models with Neutrosophic Numbers for Network Anomaly Detection and Security Defense Technology |
| title_full | Machine Learning Models with Neutrosophic Numbers for Network Anomaly Detection and Security Defense Technology |
| title_fullStr | Machine Learning Models with Neutrosophic Numbers for Network Anomaly Detection and Security Defense Technology |
| title_full_unstemmed | Machine Learning Models with Neutrosophic Numbers for Network Anomaly Detection and Security Defense Technology |
| title_short | Machine Learning Models with Neutrosophic Numbers for Network Anomaly Detection and Security Defense Technology |
| title_sort | machine learning models with neutrosophic numbers for network anomaly detection and security defense technology |
| topic | neutrosophic number security network anomaly detection cybersecurity uncertainty |
| url | https://fs.unm.edu/NSS/3AnomalyDetection.pdf |
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