FL-SATS: Federated Learning for Sybil Attack Detection in Transportation System
Document Type
Conference Proceeding
Source of Publication
IEEE International Conference on Communications
Publication Date
9-26-2025
Abstract
The Intelligent Transportation System (ITS) is advancing with enhanced vehicular networks, making security a critical concern. A major threat to these networks is Sybil attacks, where adversaries forge multiple identities to compromise the system. We propose Federated Learning for Sybil Attack Detection in Transportation Systems (FL-SATS), a mechanism leveraging federated learning for detecting Sybil attacks in ITS. FL-SATS employs a unique three-tier model aggregation at the Roadside Unit, Roadside Controller, and Software-Defined Network Controller, achieving high accuracy. Our results show that FL-SATS outperforms traditional methods with a detection accuracy of 98.7% in baseline Sybil attacks, and 98.5% in highdensity traffic. Moreover, the fuzzy logic-based vehicle selection mechanism optimizes localized training, further reducing detection latency to 20 ms and lowering communication overhead by up to 33% compared to centralized learning approaches. These results establish FL-SATS as a robust solution for securing vehicular communication.
DOI Link
ISBN
[9798331505219]
ISSN
Publisher
IEEE
First Page
3376
Last Page
3381
Disciplines
Computer Sciences
Keywords
Federated Learning, Intelligent Transportation System, Software-Defined Vehicular Network, Sybil Attack Detection
Scopus ID
Recommended Citation
Asad, Muhammad and Otoum, Safa, "FL-SATS: Federated Learning for Sybil Attack Detection in Transportation System" (2025). All Works. 7584.
https://zuscholars.zu.ac.ae/works/7584
Indexed in Scopus
yes
Open Access
no