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

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
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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

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
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
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
https://dl.acm.org/doi/10.1145/3584666
Heidari AJafari Navimipour NUnal MZhang G(2023)Machine Learning Applications in Internet-of-Drones: Systematic Review, Recent Deployments, and Open IssuesACM Computing Surveys10.1145/357172855:12(1-45)Online publication date: 3-Mar-2023
https://dl.acm.org/doi/10.1145/3571728
Perumalla SChatterjee SKumar A(2023)Modelling of oppositional Aquila Optimizer with machine learning enabled secure access control in Internet of drones environmentTheoretical Computer Science10.1016/j.tcs.2022.08.019941:C(39-54)Online publication date: 4-Jan-2023
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Hadi HCao YNisa KJamil ANi Q(2023)A comprehensive survey on security, privacy issues and emerging defence technologies for UAVsJournal of Network and Computer Applications10.1016/j.jnca.2023.103607213:COnline publication date: 1-Apr-2023
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Mekdad YAris ABabun LFergougui AConti MLazzeretti RUluagac A(2023)A survey on security and privacy issues of UAVsComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2023.109626224:COnline publication date: 1-Apr-2023
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