On the Security of Connected Vehicles: Intrusion Detection Using Federated Learning in the IoV
Document Type
Conference Proceeding
Source of Publication
ICC 2025 - IEEE International Conference on Communications
Publication Date
6-8-2025
Abstract
The expansion of Internet of Vehicles (IoV) technology brings significant cybersecurity and privacy concerns. This research study addresses cybersecurity challenges within the IoV and Intelligent Transportation System (ITS). It explores machine learning applications for detecting and preventing cyberattacks on the IoV network and autonomous vehicles, with the goal of developing and evaluating algorithms tailored to enhance IoV security, intrusion detection, and privacy. In this paper, we adopted a clustered machine learning model using Federated Learning and Convolutional Neural Networks (CNN) for IoV intrusion detection. Different scenarios involving centralized and decentralized clustering schemes are introduced. The proposed federated learning intrusion detection model has achieved a testing accuracy of 97.54 %, precision of 97.01 %, recall of 98.12 %, and an F1-score of 0.976.
DOI Link
ISSN
Publisher
IEEE
First Page
4993
Last Page
4998
Disciplines
Electrical and Computer Engineering
Keywords
Network Security and Intrusion Detection, Vehicular Ad Hoc Networks (VANETs), Privacy-Preserving Technologies in Data
Recommended Citation
Darwaish, Fadwa; Otoum, Safa; and Talaat Mouftah, Hussein, "On the Security of Connected Vehicles: Intrusion Detection Using Federated Learning in the IoV" (2025). All Works. 8361.
https://zuscholars.zu.ac.ae/works/8361
Indexed in Scopus
no
Open Access
no