The Internet of Things (IoT) consists of numerous interconnected resource-constrained devices such as sensors nodes and actuators, which are linked to the Internet. By 2020 it is anticipated that the IoT paradigm will include approximately 20 billion connected devices. The interconnection of such devices provides the ability to collect a huge amount of data for processing and analysis. A significant portion of the transacted data between IoT devices is private information, which must not in any way be eavesdropped on or tampered with. Security in IoT devices is therefore of paramount importance for further development of the technology. Such devices typically have limited area and energy resources, which makes the use of classic cryptography prohibitively expensive. Physically Unclonable Functions (PUFs) are a class of novel hardware security primitives that promise a paradigm shift in many security applications; their relatively simple architecture can answer many of the security challenges of energy-constrained IoT devices. In this paper, we discuss the design challenges of secure IoT systems; then we explain the principles of PUFs; finally we discuss the outstanding reliability and security problems of PUF technology and outline the open research questions in this field.

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Building lightweight security for low-cost pervasive devices is a major challenge considering the design requirements of a small footprint and low power consumption. Physical Unclonable Functions (PUFs) have emerged as a promising technology to provide a low-cost authentication for such devices. By exploiting intrinsic manufacturing process variations, PUFs are able to generate unique and apparently random chip identifiers. Strong-PUFs represent a variant of PUFs that have been suggested for lightweight authentication applications. Unfortunately, many of the Strong-PUFs have been shown to be susceptible to modelling attacks (i.e., using machine learning techniques) in which an adversary has access to challenge and response pairs. In this study, we propose an obfuscation technique during postprocessing of Strong-PUF responses to increase the resilience against machine learning attacks. We conduct machine learning experiments using Support Vector Machines and Artificial Neural Networks on two Strong-PUFs: a 32-bit Arbiter-PUF and a 2-XOR 32-bit Arbiter-PUF. The predictability of the 32-bit Arbiter-PUF is reduced to ≈ 70% by using an obfuscation technique. Combining the obfuscation technique with 2-XOR 32-bit Arbiter-PUF helps to reduce the predictability to ≈ 64%. More reduction in predictability has been observed in an XOR Arbiter-PUF because this PUF architecture has a good uniformity. The area overhead with an obfuscation technique consumes only 788 and 1080 gate equivalents for the 32-bit Arbiter-PUF and 2-XOR 32bit Arbiter-PUF, respectively.

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