Improving the Routing Security in Wireless Sensor Networks using Neutrosophic Set and Machine Learning Models
Numerous methods have been put forth to identify and safeguard routing data because Wireless Sensor Networks (WSNs) are susceptible to attacks during data transfer. To create an artificial intelligence-based attack detection system for WSNs, we provide a unique stochastic predictive machine learning...
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| Format: | Article |
| Language: | English |
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University of New Mexico
2025-07-01
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| Series: | Neutrosophic Sets and Systems |
| Subjects: | |
| Online Access: | https://fs.unm.edu/NSS/53WirelessSensor.pdf |
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| author | Hanadi Ahmad Simmak Ahmed A El-Douh Tareef S Alkellezli Rabih Sbera Darin shafek Ahmed Abdelhafeez |
| author_facet | Hanadi Ahmad Simmak Ahmed A El-Douh Tareef S Alkellezli Rabih Sbera Darin shafek Ahmed Abdelhafeez |
| author_sort | Hanadi Ahmad Simmak |
| collection | DOAJ |
| description | Numerous methods have been put forth to identify and safeguard routing data because Wireless Sensor Networks (WSNs) are susceptible to attacks during data transfer. To create an artificial intelligence-based attack detection system for WSNs, we provide a unique stochastic predictive machine learning technique in this research that is intended to identify unreliable events and untrustworthy routing properties. Our approach makes use of real-time feature analysis of simulated WSN routing data. We create a strong foundation for categorization. Our approach's primary benefit is the development of an effective machine learning (ML) technique that can analyze and filter WSN traffic to stop dangerous and suspicious data, lessen the significant variation in the routing information gathered, and quickly identify assaults before they happen. We use the XGBoost and Random Forest (RF) models with different parameters. Then the bipolar neutrosophic set is used to deal with uncertainty and vague information. The neutrosophic set is used to rank the ML models and select the best one. |
| format | Article |
| id | doaj-art-6ee7cc0b490d45978040a341efce66ab |
| institution | DOAJ |
| issn | 2331-6055 2331-608X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | University of New Mexico |
| record_format | Article |
| series | Neutrosophic Sets and Systems |
| spelling | doaj-art-6ee7cc0b490d45978040a341efce66ab2025-08-20T03:07:37ZengUniversity of New MexicoNeutrosophic Sets and Systems2331-60552331-608X2025-07-018790090910.5281/zenodo.15733536Improving the Routing Security in Wireless Sensor Networks using Neutrosophic Set and Machine Learning ModelsHanadi Ahmad SimmakAhmed A El-DouhTareef S AlkellezliRabih SberaDarin shafekAhmed AbdelhafeezNumerous methods have been put forth to identify and safeguard routing data because Wireless Sensor Networks (WSNs) are susceptible to attacks during data transfer. To create an artificial intelligence-based attack detection system for WSNs, we provide a unique stochastic predictive machine learning technique in this research that is intended to identify unreliable events and untrustworthy routing properties. Our approach makes use of real-time feature analysis of simulated WSN routing data. We create a strong foundation for categorization. Our approach's primary benefit is the development of an effective machine learning (ML) technique that can analyze and filter WSN traffic to stop dangerous and suspicious data, lessen the significant variation in the routing information gathered, and quickly identify assaults before they happen. We use the XGBoost and Random Forest (RF) models with different parameters. Then the bipolar neutrosophic set is used to deal with uncertainty and vague information. The neutrosophic set is used to rank the ML models and select the best one. https://fs.unm.edu/NSS/53WirelessSensor.pdfbipolar neutrosophic numbersuncertaintywireless sensor networkssecurity. attacks |
| spellingShingle | Hanadi Ahmad Simmak Ahmed A El-Douh Tareef S Alkellezli Rabih Sbera Darin shafek Ahmed Abdelhafeez Improving the Routing Security in Wireless Sensor Networks using Neutrosophic Set and Machine Learning Models Neutrosophic Sets and Systems bipolar neutrosophic numbers uncertainty wireless sensor networks security. attacks |
| title | Improving the Routing Security in Wireless Sensor Networks using Neutrosophic Set and Machine Learning Models |
| title_full | Improving the Routing Security in Wireless Sensor Networks using Neutrosophic Set and Machine Learning Models |
| title_fullStr | Improving the Routing Security in Wireless Sensor Networks using Neutrosophic Set and Machine Learning Models |
| title_full_unstemmed | Improving the Routing Security in Wireless Sensor Networks using Neutrosophic Set and Machine Learning Models |
| title_short | Improving the Routing Security in Wireless Sensor Networks using Neutrosophic Set and Machine Learning Models |
| title_sort | improving the routing security in wireless sensor networks using neutrosophic set and machine learning models |
| topic | bipolar neutrosophic numbers uncertainty wireless sensor networks security. attacks |
| url | https://fs.unm.edu/NSS/53WirelessSensor.pdf |
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