Flash-based security primitives: Evolution, challenges and future directions

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

An Overview of DRAM-Based Security Primitives

Recent developments have increased the demand for adequate security solutions, based on primitives that cannot be easily manipulated or altered, such as hardware-based primitives. Security primitives based on Dynamic Random Access Memory (DRAM) can provide cost-efficient and practical security solutions, especially for resource-constrained devices, such as hardware used in the Internet of Things (IoT), as DRAMs are an intrinsic part of most contemporary computer systems. In this work, we present a comprehensive overview of the literature regarding DRAM-based security primitives and an extended classification of it, based on a number of different criteria. In particular, first, we demonstrate the way in which DRAMs work and present the characteristics being exploited for the implementation of security primitives. Then, we introduce the primitives that can be implemented using DRAM, namely Physical Unclonable Functions (PUFs) and True Random Number Generators (TRNGs), and present the applications of each of the two types of DRAM-based security primitives. We additionally proceed to assess the security such primitives can provide, by discussing potential attacks and defences, as well as the proposed security metrics. Subsequently, we also compare these primitives to other hardware-based security primitives, noting their advantages and shortcomings, and proceed to demonstrate their potential for commercial adoption. Finally, we analyse our classification methodology, by reviewing the criteria employed in our classification and examining their significance

Theoretical and Practical Approaches for Hardness Amplification of PUFs

The era of PUFs has been characterized by the efforts put into research and the development of PUFs that are robust against attacks, in particular, machine learning (ML) attacks. In the lack of systematic and provable methods for this purpose, we have witnessed the ever-continuing competition between PUF designers/ manufacturers, cryptanalysts, and of course, adversaries that maliciously break the security of PUFs. This is despite a series of acknowledged principles developed in cryptography and complexity theory, under the umbrella term ``hardness amplification. The goal of studies on the hardness amplification is to build a strongly secure construction out of considerably weaker primitives. This paper aims at narrowing the gap between these studies and hardware security, specifically for applications in the domain of PUFs. To this end, we first review an example of practical efforts made to construct more secure PUFs, namely the concept of rolling PUFs. Based on what can be learned from this and central insights provided by the ML and complexity theory, we propose a new PUF-based scheme built around the idea of using a new function, namely, the Tribes function, which combines the outputs of a set of PUFs to generate the final response. Our theoretical findings are discussed in an exhaustive manner and supported by the results of experiments, conducted extensively on real-world PUFs