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.

ISBN

[9798331505219]

ISSN

1550-3607

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

105018463815

Indexed in Scopus

yes

Open Access

no

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