Counterfeit integrated circuits (IC) can be very harmful to the security and reliability of critical applications. Physical unclonable functions (PUFs) have been proposed as a mechanism for uniquely identifying ICs and thus reducing the prevalence of counterfeits. However, maintaining large databases of PUF challenge response pairs (CRPs) and dealing with PUF errors make it difficult to use PUFs reliably. This paper presents an innovative approach to authenticate CRPs on PUF-based ICs. The proposed method can tolerate considerable bit errors from responses of PUFs without the use of error correcting codes. Different types of optimization methods are applied to improve the overall performance. The simulation shows that it is successful in authenticating 99.96% authorized chips and filtering out 99.92% cloned chips by tolerating 12 errors in 128 bits. The results are verified with ring oscillator PUF and arbiter PUF implementations on Kintex-7 FPGA. The approach saves hardware and software resources significantly, compared to those of other authentication solutions.

In the era of ubiquitous intelligence, the Internet of Things (IoT) holds the promise as a breakthrough technology to enable diverse applications that benefit societal problems. Yet interconnecting myriad heterogeneous IoT devices across various application domains remain a security challenge. Decentralized technology has recently emerged as a powerful primitive in building distributed applications to facilitate secure transactions between mutually distrustful parties in a trustworthy manner. Unfortunately, these decentralized protocols demand computing resources and power far beyond the reach of resource-constrained IoT devices, preventing the full adoption of distributed consensus platform in the IoT setting. In this article, we address the key bottleneck to enable blockchain in resource-constrained IoT devices. We propose a lightweight implementation of proof-of-work (PoW) mining with reconfigurable hardware primitives. By replacing the hash and cryptographic functions in classic blockchain protocol with secure and efficient hardware implementations, our proposed solution can significantly reduce hardware resources and power overheads of PoW mining, while improving the transaction speed of large-scale IoT systems. Finally, we demonstrate the algorithm by proposing an antispoofing solution for GPS navigation among lightweight IoT devices. As a replacement for position computation, a mining process generates the expected coordinates with the correct initial value and function configuration.

Authentication is the act of proving that an integrated circuit (IC) is not counterfeit. One application of a physical unclonable function (PUF) circuit is to authenticate the identity of the chip using raw bits of the memory. However, several previous works present machine learning-based modeling attacks on PUFs. To alleviate this issue, we propose a novel authentication scheme involving unique DRAM power-up features using a deep Convolutional Neural Network (CNN). This methodology eliminates the need for PUFs and can authenticate DRAM technology accurately with a neural network. Our approach converts raw power-up sequence data from DRAM cells into a two-dimensional (2D) format to generate a DRAM image structure. This makes it harder for an adversary to use machine learning since there is no PUF to exploit the weaknesses. Then we apply deep CNN to DRAM images to extract unique features from each chip and classify them for authentication. Our method “DRAMNet” achieves 98.84% accuracy and 98.73% precision. The proposed technique has the advantage of a faster authentication while eliminating the need for costly error correction mechanisms and challenge-response pairs. To the best of our knowledge, this is the first method to authenticate ICs using DRAM and CNN.

Over the last two decades, hardware security has gained increasing attention in academia and industry. Flash memory has been given a spotlight in recent years, with the question of whether or not it can prove useful in a security role. Because of inherent process variation in the characteristics of flash memory modules, they can provide a unique fingerprint for a device and have thus been proposed as locations for hardware security primitives. These primitives include physical unclonable functions (PUFs), true random number generators (TRNGs), and integrated circuit (IC) counterfeit detection. In this paper, we evaluate the efficacy of flash memory-based security primitives and categorize them based on the process variations they exploit, as well as other features. We also compare and evaluate flash-based security primitives in order to identify drawbacks and essential design considerations. Finally, we describe new directions, challenges of research, and possible security vulnerabilities for flash-based security primitives that we believe would benefit from further exploration.

Online auction is one of the most successful internet business models. However, auction fraud has become the highest threat and hazard to the future of this business model. The blockchain provides a new perspective to resolve this problem. It can be used for current financial services, certificates, remittances, and online payments; meanwhile it also provides several crucial services such as smart contract, smart property, trust system, and security services. This chapter discusses how to apply blockchain to a cloud-based online auction and the principle of operation. The purpose is to fundamentally solve the problem of online fraud caused by information asymmetry of electronic transactions. To the best of the authors' knowledge, this is the first time that the blockchain has been applied to authentication of online auction. The preliminary contribution is for preventing auction fraud from the aspects of smart properties and smart contract.

A Ring Oscillator Physical Unclonable Function (RO PUF) is an application-constrained hardware security primitive that can be used for authentication and key generation. PUFs depend on variability during the fabrication process to produce random outputs that are nevertheless stable across multiple measurements. Though industry has a growing need for PUF implementations on Field Programmable Gate Arrays (FPGA) and Application-Specific Integrated Circuits (ASIC), the bit errors in PUF responses become a bottleneck and limit the usage. In this work, we comprehensively evaluate the RO PUF’s stability on FPGAs, and we propose a phase calibration process to improve the stability of RO PUFs. We also make full use of the instability of PUFs to provide a novel solution for authentication. The results show that the bit errors in our PUFs are reduced to less than 1%.