Smart intrusion detection model to identify unknown attacks for improved road safety and management
Abstract The Internet of Vehicles (IoV) has emerged as a transformative technology for intelligent transportation systems, enabling real-time communication between vehicles, infrastructure and external networks. However, this connectivity also introduces significant cybersecurity risks, such as spoo...
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-03604-5 |
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| author | Faisal Alshammari Abdullah Alsaleh |
| author_facet | Faisal Alshammari Abdullah Alsaleh |
| author_sort | Faisal Alshammari |
| collection | DOAJ |
| description | Abstract The Internet of Vehicles (IoV) has emerged as a transformative technology for intelligent transportation systems, enabling real-time communication between vehicles, infrastructure and external networks. However, this connectivity also introduces significant cybersecurity risks, such as spoofing, injection and denial of service (DoS) attacks, which threaten operational safety and system reliability. To address these challenges, this study proposes the adaptive CNN-based intrusion detection system (ACIDS), a robust and scalable framework designed to enhance intrusion detection in IoV environments. ACIDS integrates convolutional neural networks (CNN) for hierarchical feature extraction, the synthetic minority over-sampling technique (SMOTE) to address class imbalance and an open-set classification framework to detect novel attack patterns. The model was evaluated on the AWID dataset, achieving an accuracy of 94%, a perfect detection rate of 100% and a low false alarm rate of 3%. Additional validation on the NSL-KDD dataset confirmed its generalizability, with an accuracy of 91.7% and a detection rate of 98.3%. These results significantly outperform baseline models, including support vector machines (SVM) and random forests (RF), as well as recent methods such as transformer-based and hybrid RNN-CNN approaches. Key parameters used for benchmarking include accuracy, detection rate, false alarm rate, precision, F1-Score and AUC-ROC, demonstrating the model’s balanced performance and computational efficiency. By addressing critical issues such as class imbalance, adaptability to novel threats and real-time scalability, ACIDS offers a practical solution for securing IoV systems. Its low computational overhead and ability to operate on resource-constrained edge devices further emphasize its suitability for real-world deployments. |
| format | Article |
| id | doaj-art-29e450eee2db40b8be851910620edbf6 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-29e450eee2db40b8be851910620edbf62025-08-20T02:03:31ZengNature PortfolioScientific Reports2045-23222025-05-0115111010.1038/s41598-025-03604-5Smart intrusion detection model to identify unknown attacks for improved road safety and managementFaisal Alshammari0Abdullah Alsaleh1Department of Educational Studies, College of Education, Majmaah UniversityDepartment of Computer Engineering, College of Computer and Information Sciences, Majmaah UniversityAbstract The Internet of Vehicles (IoV) has emerged as a transformative technology for intelligent transportation systems, enabling real-time communication between vehicles, infrastructure and external networks. However, this connectivity also introduces significant cybersecurity risks, such as spoofing, injection and denial of service (DoS) attacks, which threaten operational safety and system reliability. To address these challenges, this study proposes the adaptive CNN-based intrusion detection system (ACIDS), a robust and scalable framework designed to enhance intrusion detection in IoV environments. ACIDS integrates convolutional neural networks (CNN) for hierarchical feature extraction, the synthetic minority over-sampling technique (SMOTE) to address class imbalance and an open-set classification framework to detect novel attack patterns. The model was evaluated on the AWID dataset, achieving an accuracy of 94%, a perfect detection rate of 100% and a low false alarm rate of 3%. Additional validation on the NSL-KDD dataset confirmed its generalizability, with an accuracy of 91.7% and a detection rate of 98.3%. These results significantly outperform baseline models, including support vector machines (SVM) and random forests (RF), as well as recent methods such as transformer-based and hybrid RNN-CNN approaches. Key parameters used for benchmarking include accuracy, detection rate, false alarm rate, precision, F1-Score and AUC-ROC, demonstrating the model’s balanced performance and computational efficiency. By addressing critical issues such as class imbalance, adaptability to novel threats and real-time scalability, ACIDS offers a practical solution for securing IoV systems. Its low computational overhead and ability to operate on resource-constrained edge devices further emphasize its suitability for real-world deployments.https://doi.org/10.1038/s41598-025-03604-5Environmental managementInternet of vehiclesIntrusion detectionConvolutional neural networkNovel threat detection |
| spellingShingle | Faisal Alshammari Abdullah Alsaleh Smart intrusion detection model to identify unknown attacks for improved road safety and management Scientific Reports Environmental management Internet of vehicles Intrusion detection Convolutional neural network Novel threat detection |
| title | Smart intrusion detection model to identify unknown attacks for improved road safety and management |
| title_full | Smart intrusion detection model to identify unknown attacks for improved road safety and management |
| title_fullStr | Smart intrusion detection model to identify unknown attacks for improved road safety and management |
| title_full_unstemmed | Smart intrusion detection model to identify unknown attacks for improved road safety and management |
| title_short | Smart intrusion detection model to identify unknown attacks for improved road safety and management |
| title_sort | smart intrusion detection model to identify unknown attacks for improved road safety and management |
| topic | Environmental management Internet of vehicles Intrusion detection Convolutional neural network Novel threat detection |
| url | https://doi.org/10.1038/s41598-025-03604-5 |
| work_keys_str_mv | AT faisalalshammari smartintrusiondetectionmodeltoidentifyunknownattacksforimprovedroadsafetyandmanagement AT abdullahalsaleh smartintrusiondetectionmodeltoidentifyunknownattacksforimprovedroadsafetyandmanagement |