Neutrosophic Set and Machine Learning Models for Detection of DoS Attack Resilience
Security has been a major problem in in-vehicle networks (VNs) in recent years, assaults that broadcast a deluge of packets, including Denial of Service (DoS) and Distributed Denial of Service (DDoS) assaults, might put the network at risk. Consequently, malicious traffic is clogging the network...
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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 |
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| Online Access: | https://fs.unm.edu/NSS/24DoSAttack.pdf |
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| author | Ahmad M. Nagm Mamdouh Gomaa Rabih Sbera Darin Shafek Ahmed A El-Douh Ahmed Abdelhafeez Ahmed E Fakhry |
| author_facet | Ahmad M. Nagm Mamdouh Gomaa Rabih Sbera Darin Shafek Ahmed A El-Douh Ahmed Abdelhafeez Ahmed E Fakhry |
| author_sort | Ahmad M. Nagm |
| collection | DOAJ |
| description | Security has been a major problem in in-vehicle networks (VNs) in recent years, assaults that broadcast a deluge of packets, including Denial of Service (DoS) and Distributed Denial of Service (DDoS) assaults, might put the network at risk. Consequently, malicious traffic is clogging the network's resources. In this regard, the literature currently in publication has offered several strategies for dealing with DoS and DDoS attacks. In contrast to the conventional methods, this work uses machine learning (ML) to suggest an intelligent intrusion detection system (IDS). To mitigate DDoS assaults, the suggested IDS makes use of an application layer dataset that is openly accessible. Then we use the neutrosophic set model to select the best ML model under different evaluation matrices. The MABAC method is used to select the best model. A neutrosophic set is used to overcome uncertainty information. Our method's experimental validation involves a thorough assessment of various machine learning models, including naïve Bayes (NB), decision trees (DT), and random forests (RF). Surprisingly, the average system accuracy of 0.99 obtained from the combined accuracy of these models outperforms current techniques. In contrast to traditional methods, our proposed IDS is highly effective and performs well in identifying DoS and DDoS attacks in VN. |
| format | Article |
| id | doaj-art-985d14c88c1f4e40aba32841cb7a4768 |
| institution | Kabale University |
| 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-985d14c88c1f4e40aba32841cb7a47682025-08-20T03:47:16ZengUniversity of New MexicoNeutrosophic Sets and Systems2331-60552331-608X2025-07-018735236110.5281/zenodo.15670718Neutrosophic Set and Machine Learning Models for Detection of DoS Attack ResilienceAhmad M. NagmMamdouh GomaaRabih SberaDarin ShafekAhmed A El-DouhAhmed AbdelhafeezAhmed E FakhrySecurity has been a major problem in in-vehicle networks (VNs) in recent years, assaults that broadcast a deluge of packets, including Denial of Service (DoS) and Distributed Denial of Service (DDoS) assaults, might put the network at risk. Consequently, malicious traffic is clogging the network's resources. In this regard, the literature currently in publication has offered several strategies for dealing with DoS and DDoS attacks. In contrast to the conventional methods, this work uses machine learning (ML) to suggest an intelligent intrusion detection system (IDS). To mitigate DDoS assaults, the suggested IDS makes use of an application layer dataset that is openly accessible. Then we use the neutrosophic set model to select the best ML model under different evaluation matrices. The MABAC method is used to select the best model. A neutrosophic set is used to overcome uncertainty information. Our method's experimental validation involves a thorough assessment of various machine learning models, including naïve Bayes (NB), decision trees (DT), and random forests (RF). Surprisingly, the average system accuracy of 0.99 obtained from the combined accuracy of these models outperforms current techniques. In contrast to traditional methods, our proposed IDS is highly effective and performs well in identifying DoS and DDoS attacks in VN. https://fs.unm.edu/NSS/24DoSAttack.pdfneutrosophic setuncertaintyvehicle networks; machine learningdenial of service (dos)distributed denial of service (ddos) |
| spellingShingle | Ahmad M. Nagm Mamdouh Gomaa Rabih Sbera Darin Shafek Ahmed A El-Douh Ahmed Abdelhafeez Ahmed E Fakhry Neutrosophic Set and Machine Learning Models for Detection of DoS Attack Resilience Neutrosophic Sets and Systems neutrosophic set uncertainty vehicle networks; machine learning denial of service (dos) distributed denial of service (ddos) |
| title | Neutrosophic Set and Machine Learning Models for Detection of DoS Attack Resilience |
| title_full | Neutrosophic Set and Machine Learning Models for Detection of DoS Attack Resilience |
| title_fullStr | Neutrosophic Set and Machine Learning Models for Detection of DoS Attack Resilience |
| title_full_unstemmed | Neutrosophic Set and Machine Learning Models for Detection of DoS Attack Resilience |
| title_short | Neutrosophic Set and Machine Learning Models for Detection of DoS Attack Resilience |
| title_sort | neutrosophic set and machine learning models for detection of dos attack resilience |
| topic | neutrosophic set uncertainty vehicle networks; machine learning denial of service (dos) distributed denial of service (ddos) |
| url | https://fs.unm.edu/NSS/24DoSAttack.pdf |
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