Numerous secure device pairing (SDP) protocols have been proposed to establish a secure communication between unidentified IoT devices that have no preshared security parameters due to the scalability requirements imposed by the ubiquitous nature of the IoT devices. In order to provide the most user-friendly IoT services, the usability assessment has become the main requirement. Thus, the complete security analysis has been replaced by a sketch of a proof to partially validate the robustness of the proposal. The few existing formal or computational security verifications on the SDP schemes have been conducted based on the assessment of a wide variety of uniquely defined security properties. Therefore, the security comparison between these protocols is not feasible and there is a lack of a unified security analysis framework to assess these pairing techniques. In this paper, we survey a selection of secure device pairing proposals that have been formally or computationally verified. We present a systematic description of the protocol assumptions, the adopted verification model, and an assessment of the verification results. In addition, we normalize the used taxonomy in order to enhance the understanding of these security validations. Furthermore, we refine the adversary capabilities on the out-of-band channel by redefining the replay capability and by introducing a new notion of delay that is dependent on the protocol structure that is more adequate for the ad hoc pairing context. Also, we propose a classification of a number of out-of-band channels based on their security properties and under our refined adversary model. Our work motivates the future SDP protocol designer to conduct a formal or a computational security assessment to allow the comparability between these pairing techniques. Furthermore, it provides a realistic abstraction of the adversary capabilities on the out-of-band channel which improves the modeling of their security characteristics in the protocol verification tools.

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.

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