Types Of Physical Unclonable Functions Research Articles

Reliability analysis and comparison of ring-PUF based on probabilistic models

—To address the issue of maladaptive reliability theory models in delay-based physical unclonable functions (PUF) of ring classes, this paper begins by analyzing the relationship between four key transistor parameters (channel length, channel width, threshold voltage, and gate oxide thickness) and delay sensitivity. Then establishes reliability quantification formulas for various PUF types, including ring oscillator PUF, k-out-of-1 PUF, Loop PUF, transient effect ring oscillator PUF, and duty cycle PUF. This analysis and comparison are conducted using the inverter delay model and probabilistic statistical methods to ensure fairness at the theoretical level. Finally, the proposed model is validated through simulation experiments, along with further analysis and comparison of ring-PUF. The results demonstrate that delay sensitivity can be utilized to assess the extent of delay impact when process parameters fluctuate. In terms of reliability, the ring-PUF can be ranked as follows: k-out-of-1 PUF, Loop PUF, ring oscillator PUF, transient effect ring oscillator PUF, and duty cycle PUF. This paper offers extensive technical guidance and theoretical support for ring PUF designers.

A comprehensive survey on hardware-assisted malware analysis and primitive techniques

Malware poses an extremely dangerous threat to the digital world, significantly impacting various domains such as smart cities, intelligent transportation, wireless sensor networks (WSNs), and the Internet of Vehicles (IoV). In the Internet of Things (IoT) context, which includes various applications, security threats, and vulnerabilities are high, especially for resource-constrained single-board devices. One of the critical challenges is the presence of unauthorised access to IoT devices that attempt to replicate authorized devices by mimicking their hardware and software specifications. This review paper provides a comprehensive overview of the security issues related to various hardware-level attacks, memory attacks, hardware Trojans, and their countermeasures in different domains. In addition, the paper addresses the study of hardware malware and explores their detection techniques for hardware-based malware, including side-channel and chip-based attacks using machine learning. Using primitive hardware techniques, such as Physically Unclonable Functions (PUFs), to generate the unique key for the device by preventing unauthorised access to the hardware during authentication for different domains is thoroughly investigated with different security features. This review analyzes the different types of PUFs, their characteristics, and their use for various security tasks, including device authentication and the key generation process. It critically evaluates the strengths and limitations of the PUF-based approach and identifies potential research directions to overcome the existing challenges.

A Graphene-Based Straintronic Physically Unclonable Function.

Physically unclonable functions (PUFs) are an integral part of modern-day hardware security. Various types of PUFs already exist, including optical, electronic, and magnetic PUFs. Here, we introduce a novel straintronic PUF (SPUF) by exploiting strain-induced reversible cracking in the contact microstructures of graphene field-effect transistors (GFETs). We found that strain cycling in GFETs with a piezoelectric gate stack and high-tensile-strength metal contacts can lead to an abrupt transition in some GFET transfer characteristics, whereas other GFETs remain resilient to strain cycling. Strain sensitive GFETs show colossal ON/OFF current ratios >107, whereas strain-resilient GFETs show ON/OFF current ratios <10. We fabricated a total of 25 SPUFs, each comprising 16 GFETs, and found near-ideal performance. SPUFs also demonstrated resilience to regression-based machine learning (ML) attacks in addition to supply voltage and temporal stability. Our findings highlight the opportunities for emerging straintronic devices in addressing some of the critical needs of the microelectronics industry.

Correlation Integral-Based Intrinsic Dimension: A Deep-Learning-Assisted Empirical Metric to Estimate the Robustness of Physically Unclonable Functions to Modeling Attacks

Physically unclonable functions (PUFs) which are robust to modeling attacks, usually have a complex, high-dimensional, nonlinear relationship between challenges and responses. Often, it is difficult to derive closed-form analytical expressions for these relationships. Consequently, it becomes difficult to compare PUF variants with regard to their robustness to modeling attacks. In this article, we apply a data-driven empirical metric termed the intrinsic dimension (ID), to estimate the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">inherent complexity</i> of the relationship between the challenges and responses of a given PUF variant. This metric is computed from the linear projection layer of a deep neural network (DNN) aimed at modeling the PUF and has a unique advantage that it is independent of the architectural details and chosen hyperparameters of the DNN. It also does not require the knowledge of the structural and functional details of the PUF. The proposed approach is evaluated using two well-known ID estimation methods based on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nearest neighbor method</i> and the full correlation integral (FCI). Through detailed experimental results, we demonstrate that the numerical values of the FCI-based ID metric for different types of PUFs have consistently high positive correlation with the perceived difficulty of modeling several common PUF variants. We also show that the ID metric provides deep insight about various subtleties that affect the robustness of PUFs to modeling attack and provides a convenient mechanism to perform a systematic comparison between different PUF compositions.

Review of Physically Unclonable Functions (PUFs): Structures, Models, and Algorithms

Physically unclonable functions (PUFs) are now an essential component for strengthening the security of Internet of Things (IoT) edge devices. These devices are an important component in many infrastructure systems such as telehealth, commerce, industry, etc. Traditionally these devices are the weakest link in the security of the system since they have limited storage, processing, and energy resources. Furthermore they are located in unsecured environments and could easily be the target of tampering and various types of attacks. We review in this work the structure of most salient types of PUF systems such as static RAM static random access memory (SRAM), ring oscillator (RO), arbiter PUFs, coating PUFs and dynamic RAM dynamic random access memory (DRAM). We discuss statistical models for the five most common types of PUFs and identify the main parameters defining their performance. We review some of the most recent algorithms that can be used to provide stable authentication and secret key generation without having to use helper data or secure sketch algorithms. Finally we provide results showing the performance of these devices and how they depend on the authentication algorithm used and the main system parameters.

Lightweight hardware fingerprinting solution using inherent memory in off-the-shelf commodity devices

An emerging technology known as Physical unclonable function (PUF) can provide a hardware root-of-trust in building the trusted computing system. PUF exploits the intrinsic process variations during the integrated circuit (IC) fabrication to generate a unique response. This unique response differs from one PUF to the other similar type of PUFs. Static random-access memory PUF (SRAM-PUF) is one of the memory-based PUFs in which the response is generated during the memory power-up process. Non-volatile memory (NVM) architecture like SRAM is available in off-the-shelf microcontroller devices. Exploiting the inherent SRAM as PUF could wide-spread the adoption of PUF. Therefore, in this study, we evaluate the suitability of inherent SRAM available in ATMega2560 microcontroller on Arduino platform as PUF that can provide a unique fingerprint. First, we analyze the start-up values (SUVs) of memory cells and select only the cells that show random values after the power-up process. Subsequently, we statistically analyze the characteristic of fifteen SRAM-PUFs which include uniqueness, reliability, and uniformity. Based on our findings, the SUVs of fifteen on-chip SRAMs achieve 42.64% uniqueness, 97.28% reliability, and 69.16% uniformity. Therefore, we concluded that the available SRAM in off-the-shelf commodity hardware has good quality to be used as PUF.

Analysis of Swarm Intelligence Based ANN Algorithms for Attacking PUFs

Physical Unclonable Functions (PUFs) are used for authentication and generation of secure cryptographic keys. However, recent research work has shown that PUFs, in general, are vulnerable to machine learning modeling attacks. From a subset of Challenge-Response Pairs (CRPs), the remaining CRPs can be effectively predicted using different machine learning algorithms. In this work, Artificial Neural Networks (ANNs) using swarm intelligence-based modeling attacks are used against different silicon-based PUFs to test their resiliency to these attacks. Amongst the swarm intelligence algorithms, the Gravitational Search Algorithm (GSA), Cuckoo Search Algorithm (CS), Particle Swarm Optimizer (PSO) and the Grey Wolf Optimizer (GWO) are used. The attacks are extensively performed on six different types of PUFs; namely, Configurable Ring Oscillator, Inverter Ring Oscillator, XOR-Inverter Ring Oscillator, Arbiter, Modified XOR-Inverter Ring Oscillator, and Hybrid Delay Based PUF. From the results, it can be concluded that the first four PUFs under study are vulnerable to ANN swarm intelligence-based models, and their responses can be predicted with an average accuracy of 71.1% to 88.3% for the different models. However, for the Hybrid Delay Based PUF and the Modified XOR-Inverter Ring Oscillator PUF, which are especially designed to thwart machine learning attacks, the prediction accuracy is much lower and in the range of 9.8% to 14.5%.

Towards a Foundation of a Mutual Authentication Protocol for a Robust and Resilient PUF-Based Communication Network

Nowadays the resiliency is the main property in any system especially communication infrastructures. Unfortunately, many developers are working on making programs more resilient and reliable, facing people who want to use leaks in an unethical way, and breaking (or trying to break) largely used one-way functions such as MD5, SHA-1, or RSA based algorithms. As an alternative, exploiting Physically Unclonable Functions (PUF) can be a good solution to improve the level of robustness and security for encryption by adding a pinch of randomness when manufacturing it, while enabling the use of true randomness. A PUF is characterized by a ‘digital fingerprint’, as an identifier, derived from a complex physical object or device, such as IoT. It is like a black box, which takes an input, called Challenge, and producing an output, called Response. Actually, they are different types of PUF, Weak and Strong, depending on the amount and the heterogeneity of challenge-responses pairs. This paper provides the fundamental concepts and techniques needed to develop a robust and resilient PUF-based communication system. This objective is achieved by mathematically defining FUFs, from foundations to the attacks, by relying on more concepts around PUF, such as cryptanalysis, or asymptotic notations. Also, we develop a mutual communication protocol that ensures security between different parties peer-to-peer devices, and devices-server. We concertize the provided fundamentals in a concrete framework that is experimentally validated.

A Dynamically Configurable PUF and Dynamic Matching Authentication Protocol

A physical unclonable function (PUF) is a hardware security primitive, which can be used secure various hardware-based applications. As a type of PUFs, strong PUFs have a large number of challenge-response pairs (CRPs), which can be used for authentication. At present, most strong PUF structures follow a one-to-one input/output relationship, i.e. linear function. As such, strong PUF designs are vulnerable to machine learning (ML) based modeling attacks. To address the issue, a dynamically configurable PUF structure is proposed in this paper. A mathematical model of the proposed dynamic PUF is presented and the design is proposed against the effective ML based attacks, such as deep neural network (DNN), logistic regression (LR) and reliability-based covariance matrix adaptation evolution strategies (CMA-ES). Experimental results on field programmable gate arrays (FPGAs) show that the proposed dynamic structure has achived good uniqueness and reliability. It is also shown that the dynamic PUF has a strong resistance to the CMA-ES attack. Due to the dynamic nature of the proposed PUF structure, an authentication protocol is also designed to generate recognizable authentication bits string. The protocol shows strong resistance to classical machine learning attacks including the new variant of CMA-ES.

Leakage free helper data storage in microcontroller based PUF implementation

Physical Unclonable Function (PUF) generates a unique identifier of a device, based on variations during a manufacturing process. Such identifier is difficult to predict or clone. It is used usually as a cipher key. Its weakness is, in general, a high sensitivity to changes in environmental conditions. In order to ensure stability, post-processing methods with error correction codes are usually applied. Unfortunately, these methods reveal, in many cases, sensitive data. We present a novel way to apply error correction code for the PUF. It is called Code Word Masking construction. This construction allows to generate PUF response in more secure way. Helper data are formed just by properly selecting PUF response bits. Therefore, helper data do not leak sensitive information. The selection is performed according to code words of the error correction code used. The method can be used for any type of weak PUF and many types of error correction codes. The error correction capability of the construction depends only on the capability of the error correction code. We describe this construction, and present an example of the PUF implementation based on the non-initialized values of the static random access memory using a 32-bit microcontroller. The implementation is more secure and has lower entropy loss compared to existing solutions. The reliability of the solution was proved through measurements under various environmental conditions. The implementation is improved by identifying and excluding the unreliable (’dark’) bits.

A PUF taxonomy

Authentication is an essential cryptographic primitive that confirms the identity of parties during communications. For security, it is important that these identities are complex, in order to make them difficult to clone or guess. In recent years, physically unclonable functions (PUFs) have emerged, in which identities are embodied in structures, rather than stored in memory elements. PUFs provide “digital fingerprints,” where information is usually read from the static entropy of a system, rather than having an identity artificially programmed in, preventing a malicious party from making a copy for nefarious use later on. Many concepts for the physical source of the uniqueness of these PUFs have been developed for multiple different applications. While certain types of PUF have received a great deal of attention, other promising suggestions may be overlooked. To remedy this, we present a review that seeks to exhaustively catalogue and provide a complete organisational scheme towards the suggested concepts for PUFs. Furthermore, by carefully considering the physical mechanisms underpinning the operation of different PUFs, we are able to form relationships between PUF technologies that previously had not been linked and look toward novel forms of PUF using physical principles that have yet to be exploited.

Voice-Based User-Device Physical Unclonable Functions for Mobile Device Authentication

In this paper, a novel voice-based User-Device (UD-) physical unclonable function (PUF) is demonstrated. In traditional PUFs, variability of challenge-response pairs (CRPs) only comes from physical randomness of silicon. Recently, a new type of PUF, touch screen-based UD-PUF was proposed, which entangles human user biometric variability with the silicon variability. Any silicon-based mobile device sensor which is a UI element can potentially seed such a UD-PUF. Having multiple orthogonal sensor space UD-PUFs helps robustness. If one UD-PUF behaves poorly in certain environmental conditions, another one might behave well. In voice UD-PUF, challenges are single words chosen by the user. The user speaks the challenge word into the microphone of the mobile device. The speech has natural human biometric variability. Raw microphone output data of analog to digital converter (ADC) also reflects the silicon variability. This voice microphone data sequence can be quantized into a binary sequence leading to a PUF—a physical randomness derived, unclonable function. To ensure reproducibility, a background noise reduction algorithm, a statistical error correction, and a frequency domain canonical representation are utilized. Both variability and reproducibility of this voice UD-PUF are evaluated. Several authentication algorithms are proposed in this paper. Pixel-matching authentication algorithm provides an average 15.33% false positive rate and an average 12.30% false negative rate while frame-count authentication algorithm provides an average 6.27% false positive rate and an average 13.23% false negative rate. For variability, we show 250+ bits Hamming distance, on average, between 512 bits binary responses of different (user, device, challenge) combinations. We also assess the pseudorandom number generation properties of voice UD-PUF by putting its binary responses through UMontreal TESTU01 suite of tests. The best voice UD-PUF algorithm passed all 26 randomness tests.

A Ring Oscillator-Based PUF With Enhanced Challenge-Response Pairs

Physical unclonable functions (PUFs) are powerful security primitives that provide cheap and secure solutions for security-related applications. Strong PUFs provide a large set of challenge-response pairs (CRPs) and are suitable for device authentication. Weak PUFs produce a small number of CRPs and can be used for key extraction. In this paper, we propose a novel method to enhance the CRP set of traditional ring oscillator-based PUFs (RO-PUFs). RO-PUFs are one of the most reliable types of PUFs and the best fit to implement on the field-programmable gate arrays. To the best of our knowledge, our method provides the maximum number of CRPs compared with the state of the art. In addition, the number of response bits that can be extracted by our method for each challenge is n-1 times more than the state of the art, where n is the number of ROs. The large number of response bits results in the authentication of more devices and generation of more keys. Evaluation of the PUF responses produced by applying our method shows a significant improvement in unpredictability and randomness compared with the related works. Moreover, we show that the responses are unique and reliable.

Optimizating Emerging Nonvolatile Memories for Dual-Mode Applications: Data Storage and Key Generator

Memory-based physical unclonable functions (PUFs) have been studied and developed as powerful primitives to generate device-specific random keys, which can be used for various security applications. However, the existing memory-based PUFs need to safely buffer the data bits in the memory before it is used to produce random bits, resulting in additional area/energy consumption and potential data security issues. In this paper, we propose a new memory-based PUF that exploits the nonvolatility and random variability of emerging memory technologies to produce random bits. Unlike conventional implementations, the random bit generation process of our proposed PUF does not disturb the data bits already stored in the memory. To satisfy the quality requirements for both memory and PUF applications, we also propose a general method to find the optimal design point of emerging nonvolatile memory (eNVM)-based PUF. An illustrative design using spin-transfer torque magnetic RAM exhibits desirable results using our method. Compared to the conventional types of memory-based PUFs, eNVM-based PUFs features enhanced security as cryptographic primitives and lower area and energy cost as data storage.

Physical Unclonable Functions and Applications: A Tutorial

This paper describes the use of physical unclonable functions (PUFs) in low-cost authentication and key generation applications. First, it motivates the use of PUFs versus conventional secure nonvolatile memories and defines the two primary PUF types: “strong PUFs” and “weak PUFs.” It describes strong PUF implementations and their use for low-cost authentication. After this description, the paper covers both attacks and protocols to address errors. Next, the paper covers weak PUF implementations and their use in key generation applications. It covers error-correction schemes such as pattern matching and index-based coding. Finally, this paper reviews several emerging concepts in PUF technologies such as public model PUFs and new PUF implementation technologies.

Statistical Analysis of MUX-Based Physical Unclonable Functions

Physical unclonable functions (PUFs) can store secret keys in integrated circuits (ICs) by exploiting the uncontrollable randomness due to manufacturing process variations. These PUFs can be used for authentication of devices and for key generation in security applications. This paper presents a rigorous statistical analysis of various types of multiplexer-based (MUX-based) PUFs including the original MUX PUF, the feed-forward MUX PUFs, the modified feed-forward MUX PUFs, and multiplexer-demultiplexer (MUX/DeMUX) PUF. The modified feed-forward MUX PUF structure is a new structure that is introduced in this paper. Three types of feed-forward PUFs are analyzed in this paper. These include feed-forward overlap, feed-forward cascade and feed-forward separate. The performance analysis quantifies interchip and intrachip variations as a function of the number of stages, the process variation variance, the environmental noise variance, and the arbiter skew for different PUFs. Three other metrics of performance are also introduced and analyzed in this paper, which include reliability, uniqueness, and randomness. A PUF is more reliable if it has less intrachip variation. A PUF is more unique if the interchip variation is closer to 50%. A PUF is more random if its response bit is 0 or 1 with equal probability. Our statistical analysis shows that the intrachip variation is less dependent on the number of stages, N, if N is greater than ten. However, the interchip variation is dependent on N if N is less than 100. It is shown that the feed-forward PUFs have higher intrachip variation than MUX PUFs; however, the modified feed-forward PUFs have significantly lower intrachip variation than the feed-forward PUFs. It is shown that the modified feed-forward cascade MUX PUF has the best uniqueness and randomness, while the original MUX PUF has the best reliability. The analysis presented in this paper can be used by the designer to choose an appropriate PUF based on the application's requirement. This eliminates the need for fabrication and testing of many PUFs for selecting an appropriate PUF.

PUF Modeling Attacks on Simulated and Silicon Data

We discuss numerical modeling attacks on several proposed strong physical unclonable functions (PUFs). Given a set of challenge-response pairs (CRPs) of a Strong PUF, the goal of our attacks is to construct a computer algorithm which behaves indistinguishably from the original PUF on almost all CRPs. If successful, this algorithm can subsequently impersonate the Strong PUF, and can be cloned and distributed arbitrarily. It breaks the security of any applications that rest on the Strong PUF's unpredictability and physical unclonability. Our method is less relevant for other PUF types such as Weak PUFs. The Strong PUFs that we could attack successfully include standard Arbiter PUFs of essentially arbitrary sizes, and XOR Arbiter PUFs, Lightweight Secure PUFs, and Feed-Forward Arbiter PUFs up to certain sizes and complexities. We also investigate the hardness of certain Ring Oscillator PUF architectures in typical Strong PUF applications. Our attacks are based upon various machine learning techniques, including a specially tailored variant of logistic regression and evolution strategies. Our results are mostly obtained on CRPs from numerical simulations that use established digital models of the respective PUFs. For a subset of the considered PUFs-namely standard Arbiter PUFs and XOR Arbiter PUFs-we also lead proofs of concept on silicon data from both FPGAs and ASICs. Over four million silicon CRPs are used in this process. The performance on silicon CRPs is very close to simulated CRPs, confirming a conjecture from earlier versions of this work. Our findings lead to new design requirements for secure electrical Strong PUFs, and will be useful to PUF designers and attackers alike.

Physical unclonable functions based on crossbar arrays for cryptographic applications

SUMMARYDue to their attractive, regular structure and their simple implementation, crossbar arrays have become one major emerging research area in the fields of nano‐devices and electronic circuits. This paper discusses novel applications of crossbars as various types of so‐called physical unclonable functions (PUFs) in the field of physical cryptography. The latter is a recent branch of cryptography and security that exploits the inherent, small‐scale randomness and disorder in physical structures. PUFs are the currently dominant primitive within this new field.In order to establish the applicability of crossbar structures as PUFs, two crossbars with rectifying junctions are investigated on the basis of real measurement data. In addition, the scalability of these crossbars with respect to their power dissipation and noise margin is evaluated in simulations. The types of PUFs as which crossbars can serve include weak PUFs and super high information content PUFs. We also discuss whether crossbar‐based PUFs allow the erasure and/or rewriting of response information on a single challenge‐response‐pair level, i.e. without affecting other PUF responses. Copyright © 2012 John Wiley &amp; Sons, Ltd.