Abstract
The demand for Internet of Things services is increasing exponentially, and consequently a large number of devices are being deployed. To efficiently authenticate these objects, the use of physical unclonable functions (PUFs) has been introduced as a promising solution for the resource-constrained nature of these devices. The use of machine learning PUF models has been recently proposed to authenticate the IoT objects while reducing the storage space requirement for each device. Nonetheless, the use of a mathematically clonable PUFs requires careful design of the enrollment process. Furthermore, the secrecy of the machine learning models used for PUFs and the scenario of leakage of sensitive information to an adversary due to an insider threat within the organization have not been discussed. In this paper, we review the state-of-the-art model-based PUF enrollment protocols. We identity two architectures of enrollment protocols based on the participating entities and the building blocks that are relevant to the security of the authentication procedure. In addition, we discuss their respective weaknesses with respect to insider and outsider threats. Our work serves as a comprehensive overview of the ML PUF-based methods and provides design guidelines for future enrollment protocol designers.
Highlights
The deployment of smart sensors is exponentially increasing to cover consumer oriented services and the requirements of industrial scenarios [1]
We focus on the authentication protocols that are based on strong physical unclonable functions (PUFs)
We have shown an example of an attack on the obfuscation technique of the OBPUF protocol that could have been mitigated through the implementation of a challenge verification component
Summary
The deployment of smart sensors is exponentially increasing to cover consumer oriented services and the requirements of industrial scenarios [1]. A final alternative is to use of a hardwarebased enrollment protocol that relies on a secure element such as a PUF [9] onboard the object This method provides a lightweight and a cost-effective authentication system that is adequate with the IoT context. The work of Pour et al [17] briefly discusses the benefits of exploiting these modeling methods in an industrial scenario These advantages include reducing the time that is required to enroll a large number of devices and the storage space that should be used to store the challenge–response pairs. It outlines the impacts of the insider attack scenario on the security of the authentication process and provides future research directions to mitigate the threats.
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