A Multiplexer based Arbiter PUF Composition with Enhanced Reliability and Security

Arbiter Physically Unclonable Function (APUF), while being relatively lightweight, is extremely vulnerable to modeling attacks. Hence, various compositions of APUFs such as XOR APUF and Lightweight Secure PUF have been proposed to be secure alternatives. Previous research has demonstrated that PUF compositions have two major challenges to overcome: vulnerability against modeling and statistical attacks, and lack of reliability. In this paper, we introduce a multiplexer based composition of APUFs, denoted as MPUF, to simultaneously overcome these challenges. In addition to the basic MPUF design, we propose two MPUF variants namely cMPUF and rMPUF to improve robustness against cryptanalysis and reliability based modeling attack, respectively. The rMPUF demonstrates enhanced robustness against reliability based modeling attack, while even the well-known XOR APUF, otherwise robust to machine learning based modeling attacks, has been modeled using the same technique with linear data and time complexities. The rMPUF can provide a good trade-off between security and hardware overhead while maintaining a significantly higher reliability level than any practical XOR APUF instance. Moreover, MPUF variants are the first APUF compositions, to the best of our knowledge, that can achieve Strict Avalanche Criterion without any additional hardware. Finally, we validate our theoretical findings using Matlab-based simulations of MPUFs

FPGA-Based PUF Designs: A Comprehensive Review and Comparative Analysis

Field-programmable gate arrays (FPGAs) have firmly established themselves as dynamic platforms for the implementation of physical unclonable functions (PUFs). Their intrinsic reconfigurability and profound implications for enhancing hardware security make them an invaluable asset in this realm. This groundbreaking study not only dives deep into the universe of FPGA-based PUF designs but also offers a comprehensive overview coupled with a discerning comparative analysis. PUFs are the bedrock of device authentication and key generation and the fortification of secure cryptographic protocols. Unleashing the potential of FPGA technology expands the horizons of PUF integration across diverse hardware systems. We set out to understand the fundamental ideas behind PUF and how crucially important it is to current security paradigms. Different FPGA-based PUF solutions, including static, dynamic, and hybrid systems, are closely examined. Each design paradigm is painstakingly examined to reveal its special qualities, functional nuances, and weaknesses. We closely assess a variety of performance metrics, including those related to distinctiveness, reliability, and resilience against hostile threats. We compare various FPGA-based PUF systems against one another to expose their unique advantages and disadvantages. This study provides system designers and security professionals with the crucial information they need to choose the best PUF design for their particular applications. Our paper provides a comprehensive view of the functionality, security capabilities, and prospective applications of FPGA-based PUF systems. The depth of knowledge gained from this research advances the field of hardware security, enabling security practitioners, researchers, and designers to make wise decisions when deciding on and implementing FPGA-based PUF solutions.publishedVersio

Deep Learning based Model Building Attacks on Arbiter PUF Compositions

Robustness to modeling attacks is an important requirement for PUF circuits. Several reported Arbiter PUF com- positions have resisted modeling attacks. and often require huge computational resources for successful modeling. In this paper we present deep feedforward neural network based modeling attack on 64-bit and 128-bit Arbiter PUF (APUF), and several other PUFs composed of Arbiter PUFs, namely, XOR APUF, Lightweight Secure PUF (LSPUF), Multiplexer PUF (MPUF) and its variants (cMPUF and rMPUF), and the recently proposed Interpose PUF (IPUF, up to the (4,4)-IPUF configuration). The technique requires no auxiliary information (e.g. side-channel information or reliability information), while employing deep neural networks of relatively low structural complexity to achieve very high modeling accuracy at low computational overhead (compared to previously proposed approaches), and is reasonably robust to error-inflicted training dataset

Systematically Quantifying Cryptanalytic Non-Linearities in Strong PUFs

Physically Unclonable Functions~(PUFs) with large challenge space~(also called Strong PUFs) are promoted for usage in authentications and various other cryptographic and security applications. In order to qualify for these cryptographic applications, the Boolean functions realized by PUFs need to possess a high non-linearity~(NL). However, with a large challenge space~(usually $\geq 64$ bits), measuring NL by classical techniques like Walsh transformation is computationally infeasible. In this paper, we propose the usage of a heuristic-based measure called non-homomorphicity test which estimates the NL of Boolean functions with high accuracy in spite of not needing access to the entire challenge-response set. We also combine our analysis with a technique used in linear cryptanalysis, called Piling-up lemma, to measure the NL of popular PUF compositions. As a demonstration to justify the soundness of the metric, we perform extensive experimentation by first estimating the NL of constituent Arbiter/Bistable Ring PUFs using the non-homomorphicity test, and then applying them to quantify the same for their XOR compositions namely XOR Arbiter PUFs and XOR Bistable Ring PUF. Our findings show that the metric explains the impact of various parameter choices of these PUF compositions on the NL obtained and thus promises to be used as an important objective criterion for future efforts to evaluate PUF designs. While the framework is not representative of the machine learning robustness of PUFs, it can be a useful complementary tool to analyze the cryptanalytic strengths of PUF primitives

Combining Optimization Objectives: New Machine-Learning Attacks on Strong PUFs

Strong Physical Unclonable Functions (PUFs), as a promising security primitive, are supposed to be a lightweight alternative to classical cryptography for purposes such as device authentication. Most of the proposed candidates, however, have been plagued by machine-learning attacks breaking their security claims. The Interpose PUF (iPUF), which has been introduced at CHES 2019, was explicitly designed with state-of-the-art machine-learning attacks in mind and is supposed to be impossible to break by classical and reliability attacks. In this paper, we analyze its vulnerability to reliability attacks. Despite the increased difficulty, these attacks are still feasible, against the original authors’ claim. We explain how adding constraints to the machine-learning objective streamlines reliability attacks and allows us to model all individual components of an iPUF successfully. In order to build a practical attack, we give several novel contributions. First, we demonstrate that reliability attacks can be performed not only with CMA-ES but also with gradient-based optimization. Second, we show that the switch to gradient-based reliability attacks makes it possible to combine reliability attacks, weight constraints, and Logistic Regression (LR) into a single optimization objective. This framework makes machine-learning attacks more efficient, as it exploits knowledge of responses and reliability information at the same time. Third, we show that a differentiable model of the iPUF exists and how it can be utilized in a combined reliability attack. We confirm that iPUFs are harder to break than regular XOR Arbiter PUFs. However, we are still able to break (1,10)-iPUF instances, which were originally assumed to be secure, with less than 10^7 PUF response queries

Strengthening Physical Unclonable Functions using Composition

We explore the idea of composing PUFs with the intent that the resultant PUF is stronger than the constituent PUFs. Prior work has proposed a construction, which sub- sequent work has shown to be weak. We revisit this prior construction and observe that it is actually weaker than previously thought when the constituent PUFs are arbiter PUFs. This weakness is demonstrated via our adaptation of the previously proposed Logistic Regression (LR) attack. We then propose new constructions called PUFs-composed-with- PUFs (P◦P). In particular, we retain a two-layer construction, but allow the same input to the composite PUF to be input to more than one constituent PUF at the first layer. We explore this family of constructions, with arbiter PUFs serving as the constituent PUFs. In particular, we identify several axes which we can vary, and empirically study the resilience of our constructions compared to the prior construction and one another from the stand- point of LR attacks. As insight in to why our family of constructions is stronger, we prove, under some idealized conditions, that the lower-bound on an attacker is indeed higher un- der our constructions than the upper-bound on an attacker for the prior construction. As such, our work suggests that composition can be a promising approach to strengthening PUFs, contrary to what prior work suggests

PUF-COTE: A PUF Construction with Challenge Obfuscation and Throughput Enhancement

Physically Unclonable Functions~(PUFs) have been a potent choice for enabling low-cost, secure communication. However, the state-of-the-art strong PUFs generate single-bit response. So, we propose PUF-COTE: a high throughput architecture based on linear feedback shift register and a strong PUF as the ``base\u27\u27-PUF. At the same time, we obfuscate the challenges to the ``base\u27\u27-PUF of the final construction. We experimentally evaluate the quality of the construction by implementing it on Artix 7 FPGAs. We evaluate the statistical quality of the responses~(using NIST SP800-92 test suit and standard PUF metrics: uniformity, uniqueness, reliability, strict avalanche criterion, ML-based modelling), which is a crucial factor for cryptographic applications