Investigating SRAM PUFs in large CPUs and GPUs

Physically unclonable functions (PUFs) provide data that can be used for cryptographic purposes: on the one hand randomness for the initialization of random-number generators; on the other hand individual fingerprints for unique identification of specific hardware components. However, today's off-the-shelf personal computers advertise randomness and individual fingerprints only in the form of additional or dedicated hardware. This paper introduces a new set of tools to investigate whether intrinsic PUFs can be found in PC components that are not advertised as containing PUFs. In particular, this paper investigates AMD64 CPU registers as potential PUF sources in the operating-system kernel, the bootloader, and the system BIOS; investigates the CPU cache in the early boot stages; and investigates shared memory on Nvidia GPUs. This investigation found non-random non-fingerprinting behavior in several components but revealed usable PUFs in Nvidia GPUs.Comment: 25 pages, 6 figures. Code in appendi

G-PUF : asoftware-only PUF for GPUs

Physical Unclonable Functions (PUFs) are security primitives which allow the generation of unique IDs and security keys. Their security stems from the inherent process variations of silicon chips manufacturing, and the minute random effects introduced in integrated circuits. PUFs usually are manufactured speciffically for this purpose, but in the last few years several proposals have developed PUFs from off-the-shelf components. These Intrinsic PUFs avoid modifications in the hardware and explore the low cost of adapting existing technologies. Graphical Processing Units (GPUs) present themselves as promising candidates for an Intrinsic PUF. GPUs are massively multi-processed systems originally built for graphical computing and more recently re-designed for general computing. These devices are distributed across a variety of systems and application environments, from computer vision platforms, to server clusters and home computers. Building PUFs with software-only strategies is a challenging problem, since a PUF must evaluate process variations without rendering system performance, characteristics which are easily done in hardware. In this work we present G-PUF, an intrinsic PUF technology running entirely on CUDA. The proposed solution maps the distribution of soft-errors in matrix multiplications when the GPU is running on adversarial conditions of overclock and undervoltage. The resulting error map will be unique to each GPU, and using a novel Challenge-Response Pair extraction algorithm, G-PUF is able to retrieve secure-keys or an device ID without disclosing information about the PUF randomness. The system was tested in real setups and requires no modifications whatsoever to an already operational GPU. G-PUF was capable of achieving upwards of 94.73% of reliability without any error correction code and can provide up to 253 unique Challenge-Response Pairs.Physically Unclonable Functions (PUFs) são primitivas de segurança que permitem a criação de identidades únicas e de chaves seguras. Sua segurança deriva das variações de processo intrínsecas à fabricação de chips de silício, e os diminutos efeitos aleatórios introduzidos em circuitos integrados. PUFs normalmente são fabricados especificamente para esse propósito, mas nos últimos anos várias propostas desenvolveram PUFs com componentes comuns. Esses PUFs Intrínsecos evitam modificações de hardware e exploram o baixo custo de adaptar tecnologias já existentes. Unidades de Processamento Gráfico (GPUs) se apresentam como candidatos promissores para um PUF Intrínseco. GPUs são sistemas massivamente multi-processados, desenvolvidos originalmente para computação gráfica e mais recentemente reprojetadas para computação genérica. Esses dispositivos estão distribuidos através de uma variedade de sistemas e aplicações, desde plataformas de visão computacional até clusters de servidores e computadores pessoais. Construir PUFs com estratégias puramente em software é um processo desafiador, já que um PUF deve avaliar variações de processo sem afetar a performance do sistema, características que são mais facilmente alcançáceis em hardware. Nesse trabalho, apresentamos o G-PUF, uma tecnologia de PUF Intrínseco rodando puramente em CUDA. A solução proposta mapeia a distribuição de soft-errors em multiplicações de matrizes, enquanto a GPU opera em condições adversas como overclock e subalimentação. O mapa de erros resultante será único para cada GPU, e utilizando um novo algorítmo para a extração de pares de desafio-resposta, o G-PUF consegue extrair chaves seguras e a identidade do dispositivo sem revelar informações sobre a sua aleatoriedade. O sistema foi testado em condições reais e não requer nenhuma modificação para um sistema de GPU já em operação. G-PUF foi capaz de alcançar uma reliability de até 94.73% sem utilizar nenhum código de correção de erros e pode prover até 253 pares de desafio-resposta únicos

Lightweight Protocols and Applications for Memory-Based Intrinsic Physically Unclonable Functions on Commercial Off-The-Shelve Devices

We are currently living in the era in which through the ever-increasing dissemination of inter-connected embedded devices, the Internet-of-Things manifests. Although such end-point devices are commonly labeled as ``smart gadgets'' and hence they suggest to implement some sort of intelligence, from a cyber-security point of view, more then often the opposite holds. The market force in the branch of commercial embedded devices leads to minimizing production costs and time-to-market. This widespread trend has a direct, disastrous impact on the security properties of such devices. The majority of currently used devices or those that will be produced in the future do not implement any or insufficient security mechanisms. Foremost the lack of secure hardware components often mitigates the application of secure protocols and applications. This work is dedicated to a fundamental solution statement, which allows to retroactively secure commercial off-the-shelf devices, which otherwise are exposed to various attacks due to the lack of secure hardware components. In particular, we leverage the concept of Physically Unclonable Functions (PUFs), to create hardware-based security anchors in standard hardware components. For this purpose, we exploit manufacturing variations in Static Random-Access Memory (SRAM) and Dynamic Random-Access Memory modules to extract intrinsic memory-based PUF instances and building on that, to develop secure and lightweight protocols and applications. For this purpose, we empirically evaluate selected and representative device types towards their PUF characteristics. In a further step, we use those device types, which qualify due to the existence of desired PUF instances for subsequent development of security applications and protocols. Subsequently, we present various software-based security solutions which are specially tailored towards to the characteristic properties of embedded devices. More precisely, the proposed solutions comprise a secure boot architecture as well as an approach to protect the integrity of the firmware by binding it to the underlying hardware. Furthermore, we present a lightweight authentication protocol which leverages a novel DRAM-based PUF type. Finally, we propose a protocol, which allows to securely verify the software state of remote embedded devices

Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Towards 6G

The next wave of wireless technologies is proliferating in connecting things among themselves as well as to humans. In the era of the Internet of things (IoT), billions of sensors, machines, vehicles, drones, and robots will be connected, making the world around us smarter. The IoT will encompass devices that must wirelessly communicate a diverse set of data gathered from the environment for myriad new applications. The ultimate goal is to extract insights from this data and develop solutions that improve quality of life and generate new revenue. Providing large-scale, long-lasting, reliable, and near real-time connectivity is the major challenge in enabling a smart connected world. This paper provides a comprehensive survey on existing and emerging communication solutions for serving IoT applications in the context of cellular, wide-area, as well as non-terrestrial networks. Specifically, wireless technology enhancements for providing IoT access in fifth-generation (5G) and beyond cellular networks, and communication networks over the unlicensed spectrum are presented. Aligned with the main key performance indicators of 5G and beyond 5G networks, we investigate solutions and standards that enable energy efficiency, reliability, low latency, and scalability (connection density) of current and future IoT networks. The solutions include grant-free access and channel coding for short-packet communications, non-orthogonal multiple access, and on-device intelligence. Further, a vision of new paradigm shifts in communication networks in the 2030s is provided, and the integration of the associated new technologies like artificial intelligence, non-terrestrial networks, and new spectra is elaborated. Finally, future research directions toward beyond 5G IoT networks are pointed out.Comment: Submitted for review to IEEE CS&

OS-level Attacks and Defenses: from Software to Hardware-based Exploits

Run-time attacks have plagued computer systems for more than three decades, with control-flow hijacking attacks such as return-oriented programming representing the long-standing state-of-the-art in memory-corruption based exploits. These attacks exploit memory-corruption vulnerabilities in widely deployed software, e.g., through malicious inputs, to gain full control over the platform remotely at run time, and many defenses have been proposed and thoroughly studied in the past. Among those defenses, control-flow integrity emerged as a powerful and effective protection against code-reuse attacks in practice. As a result, we now start to see attackers shifting their focus towards novel techniques through a number of increasingly sophisticated attacks that combine software and hardware vulnerabilities to construct successful exploits. These emerging attacks have a high impact on computer security, since they completely bypass existing defenses that assume either hardware or software adversaries. For instance, they leverage physical effects to provoke hardware faults or force the system into transient micro-architectural states. This enables adversaries to exploit hardware vulnerabilities from software without requiring physical presence or software bugs. In this dissertation, we explore the real-world threat of hardware and software-based run-time attacks against operating systems. While memory-corruption-based exploits have been studied for more than three decades, we show that data-only attacks can completely bypass state-of-the-art defenses such as Control-Flow Integrity which are also deployed in practice. Additionally, hardware vulnerabilities such as Rowhammer, CLKScrew, and Meltdown enable sophisticated adversaries to exploit the system remotely at run time without requiring any memory-corruption vulnerabilities in the system’s software. We develop novel design strategies to defend the OS against hardware-based attacks such as Rowhammer and Meltdown to tackle the limitations of existing defenses. First, we present two novel data-only attacks that completely break current code-reuse defenses deployed in real-world software and propose a randomization-based defense against such data-only attacks in the kernel. Second, we introduce a compiler-based framework to automatically uncover memory-corruption vulnerabilities in real-world kernel code. Third, we demonstrate the threat of Rowhammer-based attacks in security-sensitive applications and how to enable a partitioning policy in the system’s physical memory allocator to effectively and efficiently defend against such attacks. We demonstrate feasibility and real-world performance through our prototype for the popular and widely used Linux kernel. Finally, we develop a side-channel defense to eliminate Meltdown-style cache attacks by strictly isolating the address space of kernel and user memory

Understanding Quantum Technologies 2022

Understanding Quantum Technologies 2022 is a creative-commons ebook that provides a unique 360 degrees overview of quantum technologies from science and technology to geopolitical and societal issues. It covers quantum physics history, quantum physics 101, gate-based quantum computing, quantum computing engineering (including quantum error corrections and quantum computing energetics), quantum computing hardware (all qubit types, including quantum annealing and quantum simulation paradigms, history, science, research, implementation and vendors), quantum enabling technologies (cryogenics, control electronics, photonics, components fabs, raw materials), quantum computing algorithms, software development tools and use cases, unconventional computing (potential alternatives to quantum and classical computing), quantum telecommunications and cryptography, quantum sensing, quantum technologies around the world, quantum technologies societal impact and even quantum fake sciences. The main audience are computer science engineers, developers and IT specialists as well as quantum scientists and students who want to acquire a global view of how quantum technologies work, and particularly quantum computing. This version is an extensive update to the 2021 edition published in October 2021.Comment: 1132 pages, 920 figures, Letter forma