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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.

Routing amid malicious attackers in multihop wireless networks with unreliable links is a challenging task. In this paper, we address the fundamental problem of how to choose secure and reliable paths in such environments. We formulate the multipath routing problem as optimization problems and propose algorithms with polynomial complexity to solve them. Game theory is employed to solve and analyze the formulated multipath routing problem. We first propose the multipath routing solution minimizing the worst-case security risk (i.e., the percentage of packets captured by attackers in the worst case). While the obtained solution provides the most security routes, it may perform poorly given the unreliability of wireless links. Hence we then investigate the multipath routing solution maximizing the worst-case packet delivery ratio. As a natural extension, to achieve a tradeoff between the routing security and performance, we derive the multipath routing protocol maximizing the worst-case packet delivery ratio while limiting the worst-case security risk under given threshold. As another contribution, we establish the relationship between the worst-case security risk and packet delivery ratio, which gives the theoretical limit on the security-performance tradeoff of node-disjoint multipath routing in multihop wireless networks.