In recent years, a new generation of the Internet of Things (IoT 2.0) is emerging, based on artificial intelligence, the blockchain technology, machine learning, and the constant consolidation of pre-existing systems and subsystems into larger systems. In this work, we construct and examine a proof-of-concept prototype of such a system of systems, which consists of heterogeneous commercial off-the-shelf components, and utilises diverse communication protocols. We recognise the inherent need for lightweight security in this context, and address it by employing a low-cost state-of-the-art security solution. Our solution is based on a novel hardware and software co-engineering paradigm, utilising well-known software-based cryptographic algorithms, in order to maximise the security potential of the hardware security primitive (a Physical Unclonable Function) that is used as a security anchor. The performance of the proposed security solution is evaluated, proving its suitability even for real-time applications. Additionally, the Dolev-Yao attacker model is considered in order to assess the resilience of our solution towards attacks against the confidentiality, integrity, and availability of the examined system of systems. In this way, it is confirmed that the proposed solution is able to address the emerging security challenges of the oncoming era of systems of systems.
Entropy is a measure of uncertainty or randomness. It is the foundation for almost all cryptographic systems. True random number generators (TRNGs) and physical unclonable functions (PUFs) are the silicon primitives to respectively harvest dynamic and static entropy to generate random bit streams. In this survey paper, we present a systematic and comprehensive review of different state-of-the-art methods to harvest entropy from silicon-based devices, including the implementations, applications, and the security of the designs. Furthermore, we conclude the trends of the entropy source design to point out the current spots of entropy harvesting.
An arbiter physical unclonable function (APUF) has exponential challenge‐response pairs and is easy to implement on field‐programmable gate arrays (FPGAs). However, modeling attacks based on machine learning have become a serious threat to APUFs. Although the modeling‐attack resistance of an MA‐APUF has been improved considerably by architecture modifications, the response generation method of an MA‐APUF results in low uniqueness. In this study, we demonstrate three design problems regarding the low uniqueness that APUF‐based strong PUFs may exhibit, and we present several foundational principles to improve the uniqueness of APUF‐based strong PUFs. In particular, an improved MA‐APUF design is implemented in an FPGA and evaluated using a well‐established experimental setup. Two types of evaluation metrics are used for evaluation and comparison. Furthermore, evolution strategies, logistic regression, and K‐junta functions are used to evaluate the security of our design. The experiment results reveal that the uniqueness of our improved MA‐APUF is 81.29% (compared with that of the MA‐APUF, 13.12%), and the prediction rate is approximately 56% (compared with that of the MA‐APUF (60%‐80%).
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