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A federated learning framework for cyberattack detection in vehicular sensor networks

Driss, Maha; Almomani, Iman; e Huma, Zil; Ahmad, Jawad


Maha Driss

Iman Almomani

Zil e Huma


Vehicular Sensor Networks (VSN) introduced a new paradigm for modern transportation systems by improving traffic management and comfort. However, the increasing adoption of smart sensing technologies with the Internet of Things (IoT) made VSN a high-value target for cybercriminals. In recent years, Machine Learning (ML) and Deep Learning (DL) techniques attracted the research community to develop security solutions for IoT networks. Traditional ML and DL approaches that operate with data stored on a centralized server raise major privacy problems for user data. On the other hand, the resource-constrained nature of a smart sensing network demands lightweight security solutions. To address these issues, this article proposes a Federated Learning (FL)-based attack detection framework for VSN. The proposed scheme utilizes a group of Gated Recurrent Units (GRU) with a Random Forest (RF)-based ensembler unit. The effectiveness of the suggested framework is investigated through multiple performance metrics. Experimental findings indicate that the proposed FL approach successfully detected the cyberattacks in VSN with the highest accuracy of 99.52%. The other performance scores, precision, recall, and F1 are attained as 99.77%, 99.54%, and 99.65%, respectively.

Journal Article Type Article
Acceptance Date Feb 20, 2022
Online Publication Date Mar 24, 2022
Publication Date 2022-10
Deposit Date Apr 29, 2022
Publicly Available Date Apr 29, 2022
Journal Complex & Intelligent Systems
Print ISSN 2199-4536
Electronic ISSN 2198-6053
Publisher Springer
Peer Reviewed Peer Reviewed
Volume 8
Issue 5
Pages 4221-4235
Keywords Cybersecurity, Internet of things, Intrusion detection, Vehicular sensor networks
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