Machine learning based multi-stage intrusion detection system and feature selection ensemble security in cloud assisted vehicular ad hoc networks
Abstract The development of intelligent transportation systems relies heavily on Cloud-assisted Vehicular Ad Hoc Networks (VANETs); hence, these networks must be protected. Particularly susceptible to a broad range of assaults are VANETs because of their extreme dynamism and decentralization. Connec...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-96303-0 |
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| author | C. Christy A. Nirmala A. Mary Odilya Teena A. Isabella Amali |
| author_facet | C. Christy A. Nirmala A. Mary Odilya Teena A. Isabella Amali |
| author_sort | C. Christy |
| collection | DOAJ |
| description | Abstract The development of intelligent transportation systems relies heavily on Cloud-assisted Vehicular Ad Hoc Networks (VANETs); hence, these networks must be protected. Particularly susceptible to a broad range of assaults are VANETs because of their extreme dynamism and decentralization. Connected vehicles’ safety and efficiency could be compromised if these security threats materialize, leading to disastrous road accidents. Solving these issues will require an advanced Intrusion Detection System (IDS) with real-time threat recognition and neutralization capabilities. A new method for improving VANET security, a multi-stage Lightweight IntrusionDetection System Using Random Forest Algorithms (MLIDS-RFA), focuses on feature selection and ensemble models based on machine learning (ML). A multi-step approach is employed by the proposed system, with each stage dedicated to accurately detecting specific types of attacks. Regarding feature selection, MLIDS-RFA uses machine-learning approaches to enhance the detection process. The outcome is a reduction in the amount of processing overhead and a shortening of the response times. The detection abilities of ensemble models are enhanced by integrating the strengths of the Random Forest algorithm (RFA), which safeguards against intricate dangers. The practicality of the proposed technology is demonstrated by conducting thorough simulation analyses. This research demonstrates that the system can reduce false positives while maintaining high detection rates. This research ensures next-generation transport networks’ secure and reliable functioning and prepares the path for VANET protection upgrades. MLIDS-RFA has improved detection accuracy (96.2%) and computing efficiency (94.8%) for dynamic VANET management. It operates well with large networks (97.8%) and adapts well to network changes (93.8%). The comprehensive methodology ensures high detection performance (95.9%) and VANET security by balancing accuracy, efficiency, and scalability. |
| format | Article |
| id | doaj-art-cebd0355260c449b9b9731c692fa660a |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-cebd0355260c449b9b9731c692fa660a2025-08-20T04:03:00ZengNature PortfolioScientific Reports2045-23222025-07-0115111510.1038/s41598-025-96303-0Machine learning based multi-stage intrusion detection system and feature selection ensemble security in cloud assisted vehicular ad hoc networksC. Christy0A. Nirmala1A. Mary Odilya Teena2A. Isabella Amali3PG and Research Department of Computer Science and Artificial Intelligence, St. Joseph’s College of Arts and Science (Autonomous)Department of Computer Applications, St. Joseph’s College of Arts and Science (Autonomous)Department of Computer Applications, St. Joseph’s College of Arts and Science (Autonomous)Department of Computer Applications, St. Joseph’s College of Arts and Science (Autonomous)Abstract The development of intelligent transportation systems relies heavily on Cloud-assisted Vehicular Ad Hoc Networks (VANETs); hence, these networks must be protected. Particularly susceptible to a broad range of assaults are VANETs because of their extreme dynamism and decentralization. Connected vehicles’ safety and efficiency could be compromised if these security threats materialize, leading to disastrous road accidents. Solving these issues will require an advanced Intrusion Detection System (IDS) with real-time threat recognition and neutralization capabilities. A new method for improving VANET security, a multi-stage Lightweight IntrusionDetection System Using Random Forest Algorithms (MLIDS-RFA), focuses on feature selection and ensemble models based on machine learning (ML). A multi-step approach is employed by the proposed system, with each stage dedicated to accurately detecting specific types of attacks. Regarding feature selection, MLIDS-RFA uses machine-learning approaches to enhance the detection process. The outcome is a reduction in the amount of processing overhead and a shortening of the response times. The detection abilities of ensemble models are enhanced by integrating the strengths of the Random Forest algorithm (RFA), which safeguards against intricate dangers. The practicality of the proposed technology is demonstrated by conducting thorough simulation analyses. This research demonstrates that the system can reduce false positives while maintaining high detection rates. This research ensures next-generation transport networks’ secure and reliable functioning and prepares the path for VANET protection upgrades. MLIDS-RFA has improved detection accuracy (96.2%) and computing efficiency (94.8%) for dynamic VANET management. It operates well with large networks (97.8%) and adapts well to network changes (93.8%). The comprehensive methodology ensures high detection performance (95.9%) and VANET security by balancing accuracy, efficiency, and scalability.https://doi.org/10.1038/s41598-025-96303-0Intrusion-detectionMachine learningSecurityVehicular ad hoc networkLightweight |
| spellingShingle | C. Christy A. Nirmala A. Mary Odilya Teena A. Isabella Amali Machine learning based multi-stage intrusion detection system and feature selection ensemble security in cloud assisted vehicular ad hoc networks Scientific Reports Intrusion-detection Machine learning Security Vehicular ad hoc network Lightweight |
| title | Machine learning based multi-stage intrusion detection system and feature selection ensemble security in cloud assisted vehicular ad hoc networks |
| title_full | Machine learning based multi-stage intrusion detection system and feature selection ensemble security in cloud assisted vehicular ad hoc networks |
| title_fullStr | Machine learning based multi-stage intrusion detection system and feature selection ensemble security in cloud assisted vehicular ad hoc networks |
| title_full_unstemmed | Machine learning based multi-stage intrusion detection system and feature selection ensemble security in cloud assisted vehicular ad hoc networks |
| title_short | Machine learning based multi-stage intrusion detection system and feature selection ensemble security in cloud assisted vehicular ad hoc networks |
| title_sort | machine learning based multi stage intrusion detection system and feature selection ensemble security in cloud assisted vehicular ad hoc networks |
| topic | Intrusion-detection Machine learning Security Vehicular ad hoc network Lightweight |
| url | https://doi.org/10.1038/s41598-025-96303-0 |
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