Document Type

Article

Publication Date

2025

DOI

10.1109/OJITS.2025.3550792

Publication Title

IEEE Open Journal of Intelligent Transportation Systems

Volume

Article in Press

Pages

24 pp.

Abstract

As cyber-physical systems (CPSs) increasingly integrate physical and digital realms, securing critical infrastructure, such as the Port of Virginia, becomes paramount. Among CPSs, Unmanned Aerial Vehicles (UAVs) are vital for monitoring, communication, and supporting the command and control through remote reconnaissance and surveillance missions. These UAV applications often require coordination, planning, and runtime reconfiguration, traditionally managed by human decision-makers. However, this approach has limitations, as extensively documented in the literature. Artificial Intelligence (AI) has emerged as a pivotal tool to address these limitations, enhancing risk mitigation and informed decision-making. This research proposes a machine learning (ML) based security mechanism, leveraging federated learning and FedAvg for weight averaging, combined with SHAP analysis to identify key contributing features. This AI-based system requires less human intervention and is more effective in detecting novel attacks than traditional intrusion detection systems (IDS). Using the IEEE DataPort UAV Attack Dataset, this study aims to develop a robust distributed ML security solution for UAV swarms, significantly advancing the cybersecurity landscape for CPSs.

Rights

© 2025 The Authors.

This work is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Data Availability

Article states: "Not applicable."

Original Publication Citation

Sudhakara, S. H., & Haghnegahdar, L. (2025). Security enhancement in UAV swarms: A case study using federated learning and SHAP analysis. IEEE Open Journal of Intelligent Transportation Systems. Advance online publication. https://doi.org/10.1109/OJITS.2025.3550792

ORCID

0009-0003-8647-3932 (Haghnegahdar)

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