A Secure and Robust Machine Learning Model for Intrusion Detection in Internet of Vehicles
The rapid advancement of communication is introducing a new era for the Internet of Vehicles (IoV) in the context of Smart Cities. Although these technologies provide unparalleled connectivity and communication capabilities, they also introduce new security challenges, particularly in terms of Intru...
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2025-01-01
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| author | Pradeep Kumar Tiwari Shiv Prakash Animesh Tripathi Tiansheng Yang Rajkumar Singh Rathore Manish Aggarwal Narendra Kumar Shukla |
| author_facet | Pradeep Kumar Tiwari Shiv Prakash Animesh Tripathi Tiansheng Yang Rajkumar Singh Rathore Manish Aggarwal Narendra Kumar Shukla |
| author_sort | Pradeep Kumar Tiwari |
| collection | DOAJ |
| description | The rapid advancement of communication is introducing a new era for the Internet of Vehicles (IoV) in the context of Smart Cities. Although these technologies provide unparalleled connectivity and communication capabilities, they also introduce new security challenges, particularly in terms of Intrusion Detection. This paper presents a robust machine learning (ML) technique to enhance the security of IoV networks by developing an efficient intrusion detection system (IDS). In this paper, we proposed a fine tree-based model to study the complex behavior of network traffic inside the IoV to detect and classify anomalies for securing the IoV. The proposed fine tree-based model can be validated by conducting extensive experiments with benchmark real-world datasets which can simulate emerging IoV scenarios. The proposed Fine Tree-based IDS model, along with other models, has been evaluated using metrics such as mean accuracy, precision, recall, F1-score, specificity and error rate. The proposed model outperformed the others across each metric, achieving near-perfect results with a mean accuracy, precision, recall, F1-score, and specificity of 0.99999. However, the other models achieved mean values ranging from 0.90 to 0.98 across these metrics. Additionally, the proposed model achieved an exceptionally low mean error rate of 0.00001, while the error rates of the other models ranged from 0.02 to 0.05. The experimental findings demonstrate the superior performance of the proposed model in detecting and classifying intrusions within IoV. |
| format | Article |
| id | doaj-art-d81fc60a11e14496b56caaab19702a5e |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d81fc60a11e14496b56caaab19702a5e2025-01-31T23:04:43ZengIEEEIEEE Access2169-35362025-01-0113206782069010.1109/ACCESS.2025.353271610849545A Secure and Robust Machine Learning Model for Intrusion Detection in Internet of VehiclesPradeep Kumar Tiwari0https://orcid.org/0000-0001-8243-0941Shiv Prakash1https://orcid.org/0000-0001-7610-3004Animesh Tripathi2https://orcid.org/0009-0004-8845-3588Tiansheng Yang3https://orcid.org/0000-0001-7833-5386Rajkumar Singh Rathore4https://orcid.org/0000-0003-4571-1888Manish Aggarwal5https://orcid.org/0000-0002-3613-644XNarendra Kumar Shukla6https://orcid.org/0009-0000-5906-2335Department of Electronics and Communication Engineering, University of Allahabad, Prayagraj, IndiaDepartment of Electronics and Communication Engineering, University of Allahabad, Prayagraj, IndiaDepartment of Electronics and Communication Engineering, University of Allahabad, Prayagraj, IndiaDepartment of Creative Industries, University of South Wales, Pontypridd, U.K.Department of Computer Science, Cardiff School of Technologies, Cardiff Metropolitan University, Llandaff Campus, Cardiff, U.K.School of Artificial Intelligence and Data Science (AIDE), Indian Institute of Technology Jodhpur, Jodhpur, Rajasthan, IndiaDepartment of Electronics and Communication Engineering, University of Allahabad, Prayagraj, IndiaThe rapid advancement of communication is introducing a new era for the Internet of Vehicles (IoV) in the context of Smart Cities. Although these technologies provide unparalleled connectivity and communication capabilities, they also introduce new security challenges, particularly in terms of Intrusion Detection. This paper presents a robust machine learning (ML) technique to enhance the security of IoV networks by developing an efficient intrusion detection system (IDS). In this paper, we proposed a fine tree-based model to study the complex behavior of network traffic inside the IoV to detect and classify anomalies for securing the IoV. The proposed fine tree-based model can be validated by conducting extensive experiments with benchmark real-world datasets which can simulate emerging IoV scenarios. The proposed Fine Tree-based IDS model, along with other models, has been evaluated using metrics such as mean accuracy, precision, recall, F1-score, specificity and error rate. The proposed model outperformed the others across each metric, achieving near-perfect results with a mean accuracy, precision, recall, F1-score, and specificity of 0.99999. However, the other models achieved mean values ranging from 0.90 to 0.98 across these metrics. Additionally, the proposed model achieved an exceptionally low mean error rate of 0.00001, while the error rates of the other models ranged from 0.02 to 0.05. The experimental findings demonstrate the superior performance of the proposed model in detecting and classifying intrusions within IoV.https://ieeexplore.ieee.org/document/10849545/5GInternet of Things (IoT)Internet of Vehicles (IoV)machine learning (ML)intrusion detection system (IDS) |
| spellingShingle | Pradeep Kumar Tiwari Shiv Prakash Animesh Tripathi Tiansheng Yang Rajkumar Singh Rathore Manish Aggarwal Narendra Kumar Shukla A Secure and Robust Machine Learning Model for Intrusion Detection in Internet of Vehicles IEEE Access 5G Internet of Things (IoT) Internet of Vehicles (IoV) machine learning (ML) intrusion detection system (IDS) |
| title | A Secure and Robust Machine Learning Model for Intrusion Detection in Internet of Vehicles |
| title_full | A Secure and Robust Machine Learning Model for Intrusion Detection in Internet of Vehicles |
| title_fullStr | A Secure and Robust Machine Learning Model for Intrusion Detection in Internet of Vehicles |
| title_full_unstemmed | A Secure and Robust Machine Learning Model for Intrusion Detection in Internet of Vehicles |
| title_short | A Secure and Robust Machine Learning Model for Intrusion Detection in Internet of Vehicles |
| title_sort | secure and robust machine learning model for intrusion detection in internet of vehicles |
| topic | 5G Internet of Things (IoT) Internet of Vehicles (IoV) machine learning (ML) intrusion detection system (IDS) |
| url | https://ieeexplore.ieee.org/document/10849545/ |
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