research-article

Federated learning for drone authentication

Authors:

Abbas Yazdinejad,

Reza M. Parizi,

Ali Dehghantanha,

Hadis KarimipourAuthors Info & Claims

Volume 120, Issue C

https://doi.org/10.1016/j.adhoc.2021.102574

Published: 01 September 2021 Publication History

Abstract

The ever-rising applications of drones in the Internet of Things (IoT) era is offering many opportunities and challenges. Owing to drone abilities (silent flying, capturing photos and videos, etc.), there is widespread concern about drone authentication and which drones allow to fly. In this regard, there are several machine learning (ML) proposals for authentication in IoT networks. Such ML-based models have drawbacks in data security, privacy-preserving, and scalability when applied in drone authentication. ML-based methods collect all data and centrally train the authentication model, exposing the model to adversarial situations. This paper proposes a federated learning-based drone authentication model with drones’ Radio Frequency (RF) features in IoT networks. In the proposed model, the Deep Neural Network (DNN) architecture is implemented for drone authentication with Stochastic Gradient Descent (SGD) optimization performed locally on drones. Also, Homomorphic encryption and the secure aggregation method are applied to secure model parameters. Experimental results show that the federated drone authentication model gains a high true positive rate (TPR) during drone authentication and better performance compared to other ML-based models.

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Cited By

Torre DChennamaneni AJo JVyas GSabrsula B(2025)Toward Enhancing Privacy Preservation of a Federated Learning CNN Intrusion Detection System in IoT: Method and Empirical StudyACM Transactions on Software Engineering and Methodology10.1145/369599834:2(1-48)Online publication date: 25-Jan-2025
https://dl.acm.org/doi/10.1145/3695998
Narule YThakre K(2024)Privacy preservation using optimized Federated LearningIntelligent Decision Technologies10.3233/IDT-23010418:1(135-149)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/IDT-230104
Rao ZYou LHu GZhu F(2024)EBDTS: An Efficient BCoT-Based Data Trading System Using PUF for AuthenticationIEEE Transactions on Network and Service Management10.1109/TNSM.2024.340652421:5(5795-5808)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1109/TNSM.2024.3406524
Show More Cited By

Index Terms

Federated learning for drone authentication
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Security and privacy
  1. Network security
  2. Security services
    1. Authentication

Index terms have been assigned to the content through auto-classification.

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Information

Published In

cover image Ad Hoc Networks

Ad Hoc Networks Volume 120, Issue C

Sep 2021

123 pages

ISSN:1570-8705

Issue’s Table of Contents

Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 September 2021

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Cited By

Torre DChennamaneni AJo JVyas GSabrsula B(2025)Toward Enhancing Privacy Preservation of a Federated Learning CNN Intrusion Detection System in IoT: Method and Empirical StudyACM Transactions on Software Engineering and Methodology10.1145/369599834:2(1-48)Online publication date: 25-Jan-2025
https://dl.acm.org/doi/10.1145/3695998
Narule YThakre K(2024)Privacy preservation using optimized Federated LearningIntelligent Decision Technologies10.3233/IDT-23010418:1(135-149)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/IDT-230104
Rao ZYou LHu GZhu F(2024)EBDTS: An Efficient BCoT-Based Data Trading System Using PUF for AuthenticationIEEE Transactions on Network and Service Management10.1109/TNSM.2024.340652421:5(5795-5808)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1109/TNSM.2024.3406524
Yazdinejad ADehghantanha ASrivastava GKarimipour HParizi R(2024)Hybrid Privacy Preserving Federated Learning Against Irregular Users in Next-Generation Internet of ThingsJournal of Systems Architecture: the EUROMICRO Journal10.1016/j.sysarc.2024.103088148:COnline publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1016/j.sysarc.2024.103088
Wazzeh MArafeh MSami HOuld-Slimane HTalhi CMourad AOtrok H(2024)CRSFLJournal of Network and Computer Applications10.1016/j.jnca.2024.103987231:COnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.jnca.2024.103987
Alqahtani HKumar G(2024)Machine learning for enhancing transportation securityEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107667129:COnline publication date: 16-May-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.107667
Kumar NChaudhary A(2024)Surveying cybersecurity vulnerabilities and countermeasures for enhancing UAV securityComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2024.110695252:COnline publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.comnet.2024.110695
Sarıkaya BBahtiyar Ş(2024)A survey on security of UAV and deep reinforcement learningAd Hoc Networks10.1016/j.adhoc.2024.103642164:COnline publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1016/j.adhoc.2024.103642
Jagarlamudi GYazdinejad AParizi RPouriyeh S(2024)Exploring privacy measurement in federated learningThe Journal of Supercomputing10.1007/s11227-023-05846-480:8(10511-10551)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s11227-023-05846-4
Rabieinejad EYazdinejad AParizi RDehghantanha A(2023)Generative Adversarial Networks for Cyber Threat Hunting in Ethereum BlockchainDistributed Ledger Technologies: Research and Practice10.1145/35846662:2(1-19)Online publication date: 8-Jun-2023
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