Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET Attacks
Vehicular ad hoc networks (VANETs) aim to manage traffic, prevent accidents, and regulate various parts of traffic. However, owing to their nature, the security of VANETs remains a significant concern. This study provides insightful information regarding VANET vulnerabilities and attacks. It investi...
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| Format: | Article |
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MDPI AG
2025-06-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/16/6/324 |
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| author | Muawia A. Elsadig Abdelrahman Altigani Yasir Mohamed Abdul Hakim Mohamed Akbar Kannan Mohamed Bashir Mousab A. E. Adiel |
| author_facet | Muawia A. Elsadig Abdelrahman Altigani Yasir Mohamed Abdul Hakim Mohamed Akbar Kannan Mohamed Bashir Mousab A. E. Adiel |
| author_sort | Muawia A. Elsadig |
| collection | DOAJ |
| description | Vehicular ad hoc networks (VANETs) aim to manage traffic, prevent accidents, and regulate various parts of traffic. However, owing to their nature, the security of VANETs remains a significant concern. This study provides insightful information regarding VANET vulnerabilities and attacks. It investigates a number of security models that have recently been introduced to counter VANET security attacks with a focus on machine learning detection methods. This confirms that several challenges remain unsolved. Accordingly, this study introduces a lightweight machine learning model with a gain information feature selection method to detect VANET attacks. A balanced version of the well-known and recent dataset CISDS2017 was developed by applying a random oversampling technique. The developed dataset was used to train, test, and evaluate the proposed model. In other words, two layers of enhancements were applied—using a suitable feature selection technique and fixing the dataset imbalance problem. The results show that the proposed model, which is based on the Random Forest (RF) classifier, achieved excellent performance in terms of classification accuracy, computational cost, and classification error. It achieved an accuracy rate of 99.8%, outperforming all benchmark classifiers, including AdaBoost, decision tree (DT), K-nearest neighbors (KNNs), and multi-layer perceptron (MLP). To the best of our knowledge, this model outperforms all the existing classification techniques. In terms of processing cost, it consumes the least processing time, requiring only 69%, 59%, 35%, and 1.4% of the AdaBoost, DT, KNN, and MLP processing times, respectively. It causes negligible classification errors. |
| format | Article |
| id | doaj-art-a0834b932f9540118f1c68d8013bd0c3 |
| institution | Matheson Library |
| issn | 2032-6653 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-a0834b932f9540118f1c68d8013bd0c32025-06-25T14:31:35ZengMDPI AGWorld Electric Vehicle Journal2032-66532025-06-0116632410.3390/wevj16060324Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET AttacksMuawia A. Elsadig0Abdelrahman Altigani1Yasir Mohamed2Abdul Hakim Mohamed3Akbar Kannan4Mohamed Bashir5Mousab A. E. Adiel6College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 34212, Saudi ArabiaComputer Information Science, Higher Colleges of Technology, Al Ain 17561, United Arab EmiratesDepartment of Information Systems and Business Analytics, College of Business Administration, A Sharqiyah University, Ibra 400, OmanDepartment of Information Systems and Business Analytics, College of Business Administration, A Sharqiyah University, Ibra 400, OmanDepartment of Information Systems and Business Analytics, College of Business Administration, A Sharqiyah University, Ibra 400, OmanDepartment of Information Systems and Business Analytics, College of Business Administration, A Sharqiyah University, Ibra 400, OmanSudan Audit Chamber, Port Sudan 33311, SudanVehicular ad hoc networks (VANETs) aim to manage traffic, prevent accidents, and regulate various parts of traffic. However, owing to their nature, the security of VANETs remains a significant concern. This study provides insightful information regarding VANET vulnerabilities and attacks. It investigates a number of security models that have recently been introduced to counter VANET security attacks with a focus on machine learning detection methods. This confirms that several challenges remain unsolved. Accordingly, this study introduces a lightweight machine learning model with a gain information feature selection method to detect VANET attacks. A balanced version of the well-known and recent dataset CISDS2017 was developed by applying a random oversampling technique. The developed dataset was used to train, test, and evaluate the proposed model. In other words, two layers of enhancements were applied—using a suitable feature selection technique and fixing the dataset imbalance problem. The results show that the proposed model, which is based on the Random Forest (RF) classifier, achieved excellent performance in terms of classification accuracy, computational cost, and classification error. It achieved an accuracy rate of 99.8%, outperforming all benchmark classifiers, including AdaBoost, decision tree (DT), K-nearest neighbors (KNNs), and multi-layer perceptron (MLP). To the best of our knowledge, this model outperforms all the existing classification techniques. In terms of processing cost, it consumes the least processing time, requiring only 69%, 59%, 35%, and 1.4% of the AdaBoost, DT, KNN, and MLP processing times, respectively. It causes negligible classification errors.https://www.mdpi.com/2032-6653/16/6/324VANETsCICIDS2017connected vehiclescyber securityinternet of things securityvehicular ad hoc networks |
| spellingShingle | Muawia A. Elsadig Abdelrahman Altigani Yasir Mohamed Abdul Hakim Mohamed Akbar Kannan Mohamed Bashir Mousab A. E. Adiel Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET Attacks World Electric Vehicle Journal VANETs CICIDS2017 connected vehicles cyber security internet of things security vehicular ad hoc networks |
| title | Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET Attacks |
| title_full | Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET Attacks |
| title_fullStr | Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET Attacks |
| title_full_unstemmed | Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET Attacks |
| title_short | Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET Attacks |
| title_sort | connected vehicles security a lightweight machine learning model to detect vanet attacks |
| topic | VANETs CICIDS2017 connected vehicles cyber security internet of things security vehicular ad hoc networks |
| url | https://www.mdpi.com/2032-6653/16/6/324 |
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