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Privacy‐preserving federated learning cyber‐threat detection for intelligent transport systems with blockchain‐based security
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Moulahi, Tarek, Jabbar, Rateb, Alabdulatif, Abdulatif, Abbas, Sidra, El Khediri, Salim, Zidi, Salah and Rizwan, Muhammad (2023) Privacy‐preserving federated learning cyber‐threat detection for intelligent transport systems with blockchain‐based security. Expert Systems, 40 (5). e13103. doi:10.1111/exsy.13103 ISSN 1468-0394.
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Official URL: https://doi.org/10.1111/exsy.13103
Abstract
Artificial intelligence (AI) techniques implemented at a large scale in intelligent transport systems (ITS), have considerably enhanced the vehicles' autonomous behaviour in making independent decisions about cyber threats, attacks, and faults. While, AI techniques are based on data sharing among the vehicles, it is important to note that sensitive data cannot be shared. Thus, federated learning (FL) has been implemented to protect privacy in vehicles. On the other hand, the integrity of data and the safety of aggregation are ensured by using blockchain technology. This paper applied classification approaches to VANET and ITS cyber-threats detection at the vehicle. Subsequently, by using blockchain and by applying an aggregation strategy to different models, models from the previous step were uploaded in a smart contract. Lastly, we returned the updated models to the vehicles. Furthermore, we conducted an experimental study to measure the effectiveness of the proposed prototype. In this paper, the VeReMi data set was distributed in a balanced manner into five parts in the experimental study. Thus, classification techniques were executed by each vehicle separately, and models were generated. Upon the aggregation of the models in blockchain, they were returned to the vehicles. Lastly, the vehicles updated their decision functions and accessed the precision and accuracy of cyber-threat detection. The results indicated that the precision and accuracy decreased by 7.1% on average with comparable F1-score and recall. Our solution ensures the privacy preservation of vehicles whereas blockchain guarantees the safety of aggregation technique and low gas consumption.
Item Type: | Journal Article | ||||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||
SWORD Depositor: | Library Publications Router | ||||||||
Journal or Publication Title: | Expert Systems | ||||||||
Publisher: | Wiley | ||||||||
ISSN: | 1468-0394 | ||||||||
Official Date: | June 2023 | ||||||||
Dates: |
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Volume: | 40 | ||||||||
Number: | 5 | ||||||||
Article Number: | e13103 | ||||||||
DOI: | 10.1111/exsy.13103 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Reuse Statement (publisher, data, author rights): | This is the peer reviewed version of the following article: Moulahi, T., Jabbar, R., Alabdulatif, A., Abbas, S., El Khediri, S., Zidi, S., & Rizwan, M. (2022). Privacy-preserving federated learning cyber-threat detection for intelligent transport systems with blockchain-based security. Expert Systems, e13103., which has been published in final form at https://doi.org/10.1111/exsy.13103. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Copyright Holders: | © 2022 John Wiley & Sons Ltd. | ||||||||
Date of first compliant deposit: | 11 August 2022 | ||||||||
Date of first compliant Open Access: | 24 July 2023 | ||||||||
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