Physically Unclonable Functions (PUFs) provide a streamlined solution for lightweight device authentication. Delay-based Arbiter PUFs, with their ease of implementation and vast challenge space, have received significant attention; however, they are not immune to modelling attacks that exploit correlations between their inputs and outputs. Research is therefore polarized between developing modelling-resistant PUFs and devising machine learning attacks against them. This dichotomy often results in exaggerated concerns and overconfidence in PUF security, primarily because there lacks a universal tool to gauge a PUF’s security. In many scenarios, attacks require additional information, such as PUF type or configuration parameters. Alarmingly, new PUFs are often branded ‘secure’ if they lack a specific attack model upon introduction. To impartially assess the security of delay-based PUFs, we present a generic framework featuring a Mixture-of-PUF-Experts (MoPE) structure for mounting attacks on various PUFs with minimal adversarial knowledge, which provides a way to compare their performance fairly and impartially. We demonstrate the capability of our model to attack different PUF types, including the first successful attack on Heterogeneous Feed-Forward PUFs using only a reasonable amount of challenges and responses. We propose an extension version of our model, a Multi-gate Mixture-of-PUF-Experts (MMoPE) structure, facilitating multi-task learning across diverse PUFs to recognise commonalities across PUF designs. This allows a streamlining of training periods for attacking multiple PUFs simultaneously. We conclude by showcasing the potent performance of MoPE and MMoPE across a spectrum of PUF types, employing simulated, real-world unbiased, and biased data sets for analysis.

The security of computer systems can be increased effectively by using cryptographic co-processors to encapsulate secrets and speed-up the computationally intensive cryptographic functions. This can be especially advantageous for Internet of Things devices, as they usually have to be very efficient in cost, space and timing. However, these devices are also at greater risk of becoming targets of hardware attacks, as they handle sensitive data and are physically exposed to a nearly unrestricted population of users. This paper describes a modular cryptographic co-processor, allowing it to be applied in different scenarios and easily adjusted to concrete system specifications. The co-processor design is also open-source and freely available for anyone to further applications and modifications. It implements the basic cryptographic functions of symmetric encryption, hashing and a pseudo random number generation, with an interface to a true random number generator. In addition, the co-processor offers additional interfaces for key generation. A specific realization is presented in detail, compared to existing solutions, and its resilience against various attacks is discussed.

Considering the limited power and computational resources available, designing sufficiently secure systems for low-power devices is a difficult problem to tackle. With the ubiquitous adoption of the Internet of Things (IoT) not appearing to be slowing any time soon, resource-constrained security is more important than ever. Physical Unclonable Functions (PUFs) have gained momentum in recent years for their potential to enable strong security through the generation of unique identifiers based on entropy derived from unique manufacturing variations. Strong-PUFs, which are desirable for authentication protocols, have often been shown to be insecure to Machine Learning Modelling Attacks (ML-MA). Recently, some schemes have been proposed to enhance security against ML-MA through post-processing of the PUF; however, often, security is not sufficiently upheld, the scheme requires too large an additional overhead or key data must be insecurely stored in Non-Volatile Memory. In this work, we propose a generic framework for securing Strong-PUFs against ML-MA through obfuscation of challenge and response data by exploiting a DRAM-PUF to supplement a One-Way Function (OWF) which can be implemented using the available resources on an FPGA platform. Our proposed scheme enables reconfigurability, strong security and one-wayness. We conduct ML-MA using various classifiers to thoroughly evaluate the performance of our scheme across multiple 16-bit and 32-bit Arbiter-PUF (APUF) variants, showing our scheme reduces model accuracy to around 50% for each PUF (random guessing) and evaluate the properties of the final responses, demonstrating that ideal uniformity and uniqueness are maintained. Even though we demonstrate our proposal through a DRAM-PUF, our scheme can be extended to work with memory-based PUFs in general.

The overwhelmingly widespread use of Internet of Things (IoT) in different application domains brought not only benefits, but, alas, security concerns as a result of the increased attack surface and vectors. One of the most critical mechanisms in IoT infrastructure is key management. This paper reflects on the problems and challenges of existing key management systems, starting with the discussion of a recent real-world attack. We identify and elaborate on the drawbacks of security primitives based purely on physical variations and - after highlighting the problems of such systems - continue on to deduce an effective and cost-efficient key management solution for IoT systems extending the symbiotic security approach in a previous work. The symbiotic architecture combines software, firmware, and hardware resources for secure IoT while avoiding the traditional scheme of static key storage and generating entropy for key material on-the-fly via a combination of a Physical Unclonable Function (PUF) and pseudo-random bits pre-populated in firmware.

As the demand for highly secure and dependable lightweight systems increases in the modern world, Physically Unclonable Functions (PUFs) continue to promise a lightweight alternative to high-cost encryption techniques and secure key storage. While the security features promised by PUFs are highly attractive for secure system designers, they have been shown to be vulnerable to various sophisticated attacks - most notably Machine Learning (ML) based modelling attacks (ML-MA) which attempt to digitally clone the PUF behaviour and thus undermine their security. More recent ML-MA have even exploited publicly known helper data required for PUF error correction in order to predict PUF responses without requiring knowledge of response data. In response to this, research is beginning to emerge regarding the authentication of PUF devices with the assistance of ML as opposed to traditional PUF techniques of storage and comparison of pre-known Challenge-Response pairs (CRPs). In this article, we propose a classification system using ML based on a novel ‘PUF-Phenotype’ concept to accurately identify the origin and determine the validity of noisy memory-derived (DRAM) PUF responses as an alternative to helper data-reliant denoising techniques. To our best knowledge, we are thefirstto perform classification over multiple devices per model to enable a group-based PUF authentication scheme. We achieve up to 98% classification accuracy using a modified deep convolutional neural network (CNN) for feature extraction in conjunction with several well-established classifiers. We also experimentally verified the performance of our model on a Raspberry Pi device to determine the suitability of deploying our proposed model in a resource-constrained environment.

The Internet of Things (IoT) is becoming a revolutionary paradigm, moving toward ubiquity in day-to-day life and used in several applications such as smart healthcare systems, industry 4.0, critical infrastructure, etc. As with any concept that relies on wireless communication, authentication is of paramount importance in regards to security considerations. Devices in many IoT applications are severely constrained in terms of computational resources and are thus unable to utilize many modern cryptographic methods for security purposes. Physically unclonable functions (PUFs) propose to solve this issue by allowing devices to generate unique and secure digital fingerprints at extremely low computational cost. However, PUFs are vulnerable to machine learning based modeling attacks that can mathematically clone the PUFs in order to impersonate them. To address these requirements, this article introduces a new lightweight and practical anonymous authentication protocol for IoT that is resilient against machine learning attacks on PUFs.