Federated Learning-Based Architecture for Detecting Cyber Vehicular Attackers in Intelligent Transportation Systems

Aljabri, Dalia Abdulrahim Mokheef, Abdulhasan, Maki Mahdi, Obeid, Noor R.

Innovative Multidisciplinary Journal of Applied Technology · 2024-yil

Annotatsiya

The Internet of Vehicles (IoV) concept within Intelligent Transportation Systems (ITS) integrates automobiles, transportation, information sharing, and traffic infrastructure management to enhance road safety. IOV can collaborate to create models through federated learning, improving performance, enhancing data privacy, and ensuring the security of local vehicle data. This paper introduces a novel Distillation-based Semi supervised (DS-FL) Model for intrusion detection. This model was demonstrated using the datasets to address the heterogeneity and diversity of devices and malicious samples. Experimental results show that the proposed system achieved 99.48%, 99.75%, 99.83%, and 99.93% accuracy in detecting various types of attacks on the ISCXIDS2012, CIC-IDS2017, CSE-CIC-IDS2018, and Car-Hacking datasets, respectively, outperforming other intrusion detection techniques. It highlights the model's effectiveness in securing intelligent transportation system networks against cyber-attacks.

Maqola ma’lumotlari
MualliflarAljabri, Dalia Abdulrahim Mokheef, Abdulhasan, Maki Mahdi, Obeid, Noor R.
JurnalInnovative Multidisciplinary Journal of Applied Technology
NashriyotТДТУ Қўқон филиали
ISSN2995-486X, 2995-486x
Nashr sanasi2024-08-05
Jild2
Son7
Betlar1-12
TilIngliz
Ilmiy sohaTexnika
Indeksga qo‘shilgan2026-08-26

Maqolaning asl sahifasi

Kalit so‘zlar

Data Analytics, Federated Learning, ITS, IOV, Vehicular Networks

Ilmiy soha

Texnika

Iqtibos keltirish

APAAljabri, Dalia Abdulrahim Mokheef, Abdulhasan, Maki Mahdi, & Obeid, Noor R. (2024). Federated Learning-Based Architecture for Detecting Cyber Vehicular Attackers in Intelligent Transportation Systems. Innovative Multidisciplinary Journal of Applied Technology, 2(7), 1-12. https://www.multijournals.org/index.php/innovative/article/view/1912
GOST (OAK)Aljabri, Dalia Abdulrahim Mokheef, Abdulhasan, Maki Mahdi, Obeid, Noor R. Federated Learning-Based Architecture for Detecting Cyber Vehicular Attackers in Intelligent Transportation Systems // Innovative Multidisciplinary Journal of Applied Technology. – 2024. – T. 2, № 7. – P. 1-12.
MLAAljabri, Dalia Abdulrahim Mokheef, Abdulhasan, Maki Mahdi, and Obeid, Noor R. “Federated Learning-Based Architecture for Detecting Cyber Vehicular Attackers in Intelligent Transportation Systems.” Innovative Multidisciplinary Journal of Applied Technology, 2(7), 1-12, 2024. https://www.multijournals.org/index.php/innovative/article/view/1912
ChicagoAljabri, Dalia Abdulrahim Mokheef, Abdulhasan, Maki Mahdi, and Obeid, Noor R. “Federated Learning-Based Architecture for Detecting Cyber Vehicular Attackers in Intelligent Transportation Systems.” Innovative Multidisciplinary Journal of Applied Technology, 2(7), 1-12 (2024). https://www.multijournals.org/index.php/innovative/article/view/1912
HarvardAljabri, Dalia Abdulrahim Mokheef, Abdulhasan, Maki Mahdi, and Obeid, Noor R (2024) ‘Federated Learning-Based Architecture for Detecting Cyber Vehicular Attackers in Intelligent Transportation Systems’, Innovative Multidisciplinary Journal of Applied Technology, 2(7), 1-12. https://www.multijournals.org/index.php/innovative/article/view/1912
BibTeX
@article{aljabridaliaabdulrahimmokheef2024,
  title = {Federated Learning-Based Architecture for Detecting Cyber Vehicular Attackers in Intelligent Transportation Systems},
  author = {Aljabri, Dalia Abdulrahim Mokheef and Abdulhasan, Maki Mahdi and Obeid, Noor R.},
  journal = {Innovative Multidisciplinary Journal of Applied Technology},
  year = {2024},
  volume = {2},
  number = {7},
  pages = {1-12},
  url = {https://www.multijournals.org/index.php/innovative/article/view/1912}
}

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