Efficient Distributed Denial of Service Attack Detection in Internet of Vehicles Using Gini Index Feature Selection and Federated Learning
Considering that smart vehicles are becoming interconnected through the Internet of Vehicles, cybersecurity threats like Distributed Denial of Service (DDoS) attacks pose a great challenge. Detection methods currently face challenges due to the complex and enormous amounts of data inherent in IoV sy...
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MDPI AG
2025-01-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/17/1/9 |
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| author | Muhammad Dilshad Madiha Haider Syed Semeen Rehman |
| author_facet | Muhammad Dilshad Madiha Haider Syed Semeen Rehman |
| author_sort | Muhammad Dilshad |
| collection | DOAJ |
| description | Considering that smart vehicles are becoming interconnected through the Internet of Vehicles, cybersecurity threats like Distributed Denial of Service (DDoS) attacks pose a great challenge. Detection methods currently face challenges due to the complex and enormous amounts of data inherent in IoV systems. This paper presents a new approach toward improving DDoS attack detection by using the Gini index in feature selection and Federated Learning during model training. The Gini index assists in filtering out important features, hence simplifying the models for higher accuracy. FL enables decentralized training across many devices while preserving privacy and allowing scalability. The results show that the case for this approach is in detecting DDoS attacks, bringing out data confidentiality, and reducing computational load. As noted in this paper, the average accuracy of the models is 91%. Moreover, different types of DDoS attacks were identified by employing our proposed technique. Precisions achieved are as follows: DrDoS_DNS: 28.65%, DrDoS_SNMP: 28.94%, DrDoS_UDP: 9.20%, and NetBIOS: 20.61%. In this research, we foresee the potential for harvesting from integrating advanced feature selection with FL so that IoV systems can meet modern cybersecurity requirements. It also provides a robust and efficient solution for the future automotive industry. By carefully selecting only the most important data features and decentralizing the model training to devices, we reduce both time and memory usage. This makes the system much faster and lighter on resources, making it perfect for real-time IoV applications. Our approach is both effective and efficient for detecting DDoS attacks in IoV environments. |
| format | Article |
| id | doaj-art-61c45ebef5624ba3af112947965214ed |
| institution | Kabale University |
| issn | 1999-5903 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj-art-61c45ebef5624ba3af112947965214ed2025-01-24T13:33:33ZengMDPI AGFuture Internet1999-59032025-01-01171910.3390/fi17010009Efficient Distributed Denial of Service Attack Detection in Internet of Vehicles Using Gini Index Feature Selection and Federated LearningMuhammad Dilshad0Madiha Haider Syed1Semeen Rehman2Institute of Information Technology, Quaid-e-Azam University, Islamabad 45320, PakistanInstitute of Information Technology, Quaid-e-Azam University, Islamabad 45320, PakistanInstitute of Computer Technology, Technical University of Vienna (TU Wien), 1040 Vienna, AustriaConsidering that smart vehicles are becoming interconnected through the Internet of Vehicles, cybersecurity threats like Distributed Denial of Service (DDoS) attacks pose a great challenge. Detection methods currently face challenges due to the complex and enormous amounts of data inherent in IoV systems. This paper presents a new approach toward improving DDoS attack detection by using the Gini index in feature selection and Federated Learning during model training. The Gini index assists in filtering out important features, hence simplifying the models for higher accuracy. FL enables decentralized training across many devices while preserving privacy and allowing scalability. The results show that the case for this approach is in detecting DDoS attacks, bringing out data confidentiality, and reducing computational load. As noted in this paper, the average accuracy of the models is 91%. Moreover, different types of DDoS attacks were identified by employing our proposed technique. Precisions achieved are as follows: DrDoS_DNS: 28.65%, DrDoS_SNMP: 28.94%, DrDoS_UDP: 9.20%, and NetBIOS: 20.61%. In this research, we foresee the potential for harvesting from integrating advanced feature selection with FL so that IoV systems can meet modern cybersecurity requirements. It also provides a robust and efficient solution for the future automotive industry. By carefully selecting only the most important data features and decentralizing the model training to devices, we reduce both time and memory usage. This makes the system much faster and lighter on resources, making it perfect for real-time IoV applications. Our approach is both effective and efficient for detecting DDoS attacks in IoV environments.https://www.mdpi.com/1999-5903/17/1/9DDoS attack detectionInternet of Vehicles (IoV)Gini indexfeature selectionFederated Learning (FL)cybersecurity |
| spellingShingle | Muhammad Dilshad Madiha Haider Syed Semeen Rehman Efficient Distributed Denial of Service Attack Detection in Internet of Vehicles Using Gini Index Feature Selection and Federated Learning Future Internet DDoS attack detection Internet of Vehicles (IoV) Gini index feature selection Federated Learning (FL) cybersecurity |
| title | Efficient Distributed Denial of Service Attack Detection in Internet of Vehicles Using Gini Index Feature Selection and Federated Learning |
| title_full | Efficient Distributed Denial of Service Attack Detection in Internet of Vehicles Using Gini Index Feature Selection and Federated Learning |
| title_fullStr | Efficient Distributed Denial of Service Attack Detection in Internet of Vehicles Using Gini Index Feature Selection and Federated Learning |
| title_full_unstemmed | Efficient Distributed Denial of Service Attack Detection in Internet of Vehicles Using Gini Index Feature Selection and Federated Learning |
| title_short | Efficient Distributed Denial of Service Attack Detection in Internet of Vehicles Using Gini Index Feature Selection and Federated Learning |
| title_sort | efficient distributed denial of service attack detection in internet of vehicles using gini index feature selection and federated learning |
| topic | DDoS attack detection Internet of Vehicles (IoV) Gini index feature selection Federated Learning (FL) cybersecurity |
| url | https://www.mdpi.com/1999-5903/17/1/9 |
| work_keys_str_mv | AT muhammaddilshad efficientdistributeddenialofserviceattackdetectionininternetofvehiclesusingginiindexfeatureselectionandfederatedlearning AT madihahaidersyed efficientdistributeddenialofserviceattackdetectionininternetofvehiclesusingginiindexfeatureselectionandfederatedlearning AT semeenrehman efficientdistributeddenialofserviceattackdetectionininternetofvehiclesusingginiindexfeatureselectionandfederatedlearning |