A methodology to identify identical single-board computers based on hardware behavior fingerprinting
Pedro Miguel S\'anchez S\'anchez, Jos\'e Mar\'ia Jorquera Valero,, Alberto Huertas Celdr\'an, G\'er\^ome Bovet, Manuel Gil P\'erez, Gregorio, Mart\'inez P\'erez

TL;DR
This paper presents a new behavioral fingerprinting methodology using ML/DL to identify and distinguish identical single-board computers, addressing cybersecurity threats in IoT environments.
Contribution
It introduces a novel approach that considers hardware/software limitations and properties like stability and robustness for identifying identical single-board devices.
Findings
Achieved 91.9% true positive rate in real environment
Successfully identified all devices with a 50% threshold
Highlights the importance of stability and diversity in fingerprinting
Abstract
The connectivity and resource-constrained nature of single-board devices open the door to cybersecurity concerns affecting Internet of Things (IoT) scenarios. One of the most important issues is the presence of unauthorized IoT devices that want to impersonate legitimate ones by using identical hardware and software specifications. This situation can provoke sensitive information leakages, data poisoning, or privilege escalation in IoT scenarios. Combining behavioral fingerprinting and Machine/Deep Learning (ML/DL) techniques is a promising approach to identify these malicious spoofing devices by detecting minor performance differences generated by imperfections in manufacturing. However, existing solutions are not suitable for single-board devices since they do not consider their hardware and software limitations, underestimate critical aspects such as fingerprint stability or context…
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Taxonomy
TopicsDigital Media Forensic Detection · Physical Unclonable Functions (PUFs) and Hardware Security · Advanced Malware Detection Techniques
