Integrating Physical Unclonable Functions with Machine Learning for the Authentication of Edge Devices in IoT Networks

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Detalles Bibliográficos
Publicado en:	Future Internet vol. 17, no. 7 (2025), p. 275-310
Autor Principal:	Sheikh Abdul Manan
Outros autores:	Islam, Md Rafiqul, Habaebi Mohamed Hadi, Zabidi Suriza Ahmad, Bin Najeeb Athaur Rahman, Kabbani Adnan
Publicado:	MDPI AG
Materias:	Collaboration Multilayers Communication Cementing Modelling Multilayer perceptrons Real time Edge computing Data processing Privacy Machine learning Access control Internet of Things Field programmable gate arrays Business metrics Accuracy Data integrity Performance measurement Artificial intelligence Integrated circuits Support vector machines Reliability Sensors Decision making Design Blockchain Integrated approach Cloud computing Authentication Data transmission Cybersecurity
Acceso en liña:	Citation/Abstract Full Text + Graphics Full Text - PDF
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Descripción
Resumo:	Edge computing (EC) faces unique security threats due to its distributed architecture, resource-constrained devices, and diverse applications, making it vulnerable to data breaches, malware infiltration, and device compromise. The mitigation strategies against EC data security threats include encryption, secure authentication, regular updates, tamper-resistant hardware, and lightweight security protocols. Physical Unclonable Functions (PUFs) are digital fingerprints for device authentication that enhance interconnected devices’ security due to their cryptographic characteristics. PUFs produce output responses against challenge inputs based on the physical structure and intrinsic manufacturing variations of an integrated circuit (IC). These challenge-response pairs (CRPs) enable secure and reliable device authentication. Our work implements the Arbiter PUF (APUF) on Altera Cyclone IV FPGAs installed on the ALINX AX4010 board. The proposed APUF has achieved performance metrics of 49.28% uniqueness, 38.6% uniformity, and 89.19% reliability. The robustness of the proposed APUF against machine learning (ML)-based modeling attacks is tested using supervised Support Vector Machines (SVMs), logistic regression (LR), and an ensemble of gradient boosting (GB) models. These ML models were trained over more than 19K CRPs, achieving prediction accuracies of 61.1%, 63.5%, and 63%, respectively, thus cementing the resiliency of the device against modeling attacks. However, the proposed APUF exhibited its vulnerability to Multi-Layer Perceptron (MLP) and random forest (RF) modeling attacks, with 95.4% and 95.9% prediction accuracies, gaining successful authentication. APUFs are well-suited for device authentication due to their lightweight design and can produce a vast number of challenge-response pairs (CRPs), even in environments with limited resources. Our findings confirm that our approach effectively resists widely recognized attack methods to model PUFs.
ISSN:	1999-5903
DOI:	10.3390/fi17070275
Fonte:	ABI/INFORM Global