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Some of the main challenges towards utilizing conventional cryptographictechniques in Internet of Things (IoT) include the need for generating secretkeys for such a large-scale network, distributing the generated keys to all thedevices, key storage as well as the vulnerability to security attacks when anadversary gets physical access to the devices. In this paper, a novel secretkey generation method is proposed for IoTs that utilize the intrinsicrandomness embedded in the devices' memories introduced in the manufacturingprocess. A fuzzy extractor structure using serially concatenated BCH-Polarcodes is proposed to generate reproducible keys from a ReRAM-based\emph{ternary-state} Physical Unclonable Functions (PUFs) for deviceauthentication and secret key generation. The ReRAM based PUFs are the mostpractical choice for authentication and key generation in IoT, as they operateat or below the systems' noise level and therefore are less vulnerable to sidechannel attacks compared to the alternative memory technologies. However, thecurrent ReRAM-based PUFs present a high false negative authentication ratesince the behavior of these devices can vary in different physical conditionsthat results in a low probability of regenerating the same response indifferent attempts. In this paper, we propose a secret key generation schemefor ternary state PUFs that enables reliable reconstruction of the desiredsecret keys utilizing a serially concatenated BCH-Polar fuzzy extractor. Theexperimental results show that the proposed model can offer a significantlylower probability of mismatch between the original key and the regeneratedones, while a less number of \textit{Helper data} bits were used to extract the\textit{Key} when compared to previously proposed fuzzy extractor techniques.

A Secret Key Generation Scheme for Internet of Things using Ternary-States ReRAM-based Physical Unclonable Functions