FL-SATS: Federated Learning for Sybil Attack Detection in Transportation System

Muhammad Asad; Safa Otoum

doi:10.1109/ICC52391.2025.11161123

FL-SATS: Federated Learning for Sybil Attack Detection in Transportation System

Muhammad Asad
, Safa Otoum

Zayed University

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings published in a book › peer-review

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.

Original language	English
Title of host publication	ICC 2025 - IEEE International Conference on Communications
Editors	Matthew Valenti, David Reed, Melissa Torres
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3376-3381
Number of pages	6
ISBN (Electronic)	9798331505219
DOIs	https://doi.org/10.1109/ICC52391.2025.11161123
Publication status	Published - 2025
Externally published	Yes
Event	2025 IEEE International Conference on Communications, ICC 2025 - Montreal, Canada Duration: 8 Jun 2025 → 12 Jun 2025

Publication series

Name	IEEE International Conference on Communications
ISSN (Print)	1550-3607

Conference

Conference	2025 IEEE International Conference on Communications, ICC 2025
Country/Territory	Canada
City	Montreal
Period	8/06/25 → 12/06/25

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

ASJC Scopus subject areas

Computer Networks and Communications
Electrical and Electronic Engineering

Keywords

Federated Learning
Intelligent Transportation System
Software-Defined Vehicular Network
Sybil Attack Detection

Access to Document

10.1109/ICC52391.2025.11161123

Cite this

Asad, M., & Otoum, S. (2025). FL-SATS: Federated Learning for Sybil Attack Detection in Transportation System. In M. Valenti, D. Reed, & M. Torres (Eds.), ICC 2025 - IEEE International Conference on Communications (pp. 3376-3381). (IEEE International Conference on Communications). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICC52391.2025.11161123

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title = "FL-SATS: Federated Learning for Sybil Attack Detection in Transportation System",

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.",

keywords = "Federated Learning, Intelligent Transportation System, Software-Defined Vehicular Network, Sybil Attack Detection",

author = "Muhammad Asad and Safa Otoum",

note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 IEEE International Conference on Communications, ICC 2025 ; Conference date: 08-06-2025 Through 12-06-2025",

year = "2025",

doi = "10.1109/ICC52391.2025.11161123",

language = "English",

series = "IEEE International Conference on Communications",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "3376--3381",

editor = "Matthew Valenti and David Reed and Melissa Torres",

booktitle = "ICC 2025 - IEEE International Conference on Communications",

address = "United States",

}

Asad, M & Otoum, S 2025, FL-SATS: Federated Learning for Sybil Attack Detection in Transportation System. in M Valenti, D Reed & M Torres (eds), ICC 2025 - IEEE International Conference on Communications. IEEE International Conference on Communications, Institute of Electrical and Electronics Engineers Inc., pp. 3376-3381, 2025 IEEE International Conference on Communications, ICC 2025, Montreal, Canada, 8/06/25. https://doi.org/10.1109/ICC52391.2025.11161123

FL-SATS: Federated Learning for Sybil Attack Detection in Transportation System. / Asad, Muhammad; Otoum, Safa.
ICC 2025 - IEEE International Conference on Communications. ed. / Matthew Valenti; David Reed; Melissa Torres. Institute of Electrical and Electronics Engineers Inc., 2025. p. 3376-3381 (IEEE International Conference on Communications).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings published in a book › peer-review

TY - GEN

T1 - FL-SATS

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AU - Asad, Muhammad

AU - Otoum, Safa

PY - 2025

Y1 - 2025

N2 - 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.

AB - 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.

KW - Federated Learning

KW - Intelligent Transportation System

KW - Software-Defined Vehicular Network

KW - Sybil Attack Detection

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M3 - Conference proceedings published in a book

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ER -

FL-SATS: Federated Learning for Sybil Attack Detection in Transportation System

Abstract

Publication series

Conference

UN SDGs

ASJC Scopus subject areas

Keywords

Access to Document

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