15 research outputs found

    MEMS sensors as physical unclonable functions

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    A fundamental requirement of any crypto system is that secret-key material remains securely stored so that it is robust in withstanding attacks including physical tampering. In this context, physical unclonable functions (PUFs) have been proposed to store cryptographic secrets in a particularly secure manner. In this thesis, the feasibility of using microelectromechanical systems (MEMS) sensors for secure key storage purposes is evaluated for the first time. To this end, we investigated an off-the-shelf 3-axis MEMS gyroscope design and used its properties to derive a unique fingerprint from each sensor. We thoroughly examined the robustness of the derived fingerprints against temperature variation and aging. We extracted stable keys with nearly full entropy from the fingerprints. The security level of the extracted keys lies in a range between 27 bits and 150 bits depending on the applied test conditions and the used entropy estimation method. Moreover, we provide experimental evidence that the extractable key length is higher in practice when multiple wafers are considered. In addition, it is shown that further improvements could be achieved by using more precise measurement techniques and by optimizing the MEMS design. The robustness of a MEMS PUF against tampering and malicious read-outs was tested by three different types of physical attacks. We could show that MEMS PUFs provide a high level of protection due to the sensitivity of their characteristics to disassembly.Eine grundlegende Anforderung jedes Kryptosystems ist, dass der verwendete geheime Schlüssel sicher und geschützt aufbewahrt wird. Vor diesem Hintergrund wurden physikalisch unklonbare Funktionen (PUFs) vorgeschlagen, um kryptographische Geheimnisse besonders sicher zu speichern. In dieser Arbeit wird erstmals die Verwendbarkeit von mikroelektromechanischen Systemen (MEMS) für die sichere Schlüsselspeicherung anhand eines 3-achsigen MEMS Drehratensensor gezeigt. Dabei werden die Eigenschaften der Sensoren zur Ableitung eines eindeutigen Fingerabdrucks verwendet. Die Temperatur- und Langzeitstabilität der abgeleiteten Fingerabdrücke wurde ausführlich untersucht. Aus den Fingerabdrücken wurden stabile Schlüssel mit einem Sicherheitsniveau zwischen 27 Bit und 150 Bit, abhängig von den Testbedingungen und der verwendeten Entropie-Schätzmethode, extrahiert. Außerdem konnte gezeigt werden, dass die Schlüssellänge ansteigt, je mehr Wafer betrachtet werden. Darüber hinaus wurde die Verwendung einer präziseren Messtechnik und eine Optimierung des MEMS-Designs als potentielle Verbesserungsmaßnahmen identifiziert. Die Robustheit einer MEMS PUF gegen Manipulationen und feindseliges Auslesen durch verschiedene Arten von physikalischen Angriffen wurde untersucht. Es konnte gezeigt werden, dass MEMS PUFs aufgrund der Empfindlichkeit ihrer Eigenschaften hinsichtlich einer Öffnung des Mold-Gehäuses eine hohe Widerstandsfähigkeit gegenüber invasiven Angriffen aufweisen

    Novel Transistor Resistance Variation-based Physical Unclonable Functions with On-Chip Voltage-to-Digital Converter Designed for Use in Cryptographic and Authentication Applications

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    Security mechanisms such as encryption, authentication, and feature activation depend on the integrity of embedded secret keys. Currently, this keying material is stored as digital bitstrings in non-volatile memory on FPGAs and ASICs. However, secrets stored this way are not secure against a determined adversary, who can use specialized probing attacks to uncover the secret. Furthermore, storing these pre-determined bitstrings suffers from the disadvantage of not being able to generate the key only when needed. Physical Unclonable Functions (PUFs) have emerged as a superior alternative to this. A PUF is an embedded Integrated Circuit (IC) structure that is designed to leverage random variations in physical parameters of on-chip components as the source of entropy for generating random and unique bitstrings. PUFs also incorporate an on-chip infrastructure for measuring and digitizing these variations in order to produce bitstrings. Additionally, PUFs are designed to reproduce a bitstring on-demand and therefore eliminate the need for on-chip storage. In this work, two novel PUFs are presented that leverage the random variations observed in the resistance of transistors. A thorough analysis of the randomness, uniqueness and stability characteristics of the bitstrings generated by these PUFs is presented. All results shown are based on an exhaustive testing of a set of 63 chips designed with numerous copies of the PUFs on each chip and fabricated in a 90nm nine-metal layer technology. An on-chip voltage-to-digital conversion technique is also presented and tested on the set of 63 chips. Statistical results of the bitstrings generated by the on-chip digitization technique are compared with that of the voltage-derived bitstrings to evaluate the efficacy of the digitization technique. One of the most important quality metrics of the PUF and the on-chip voltage-to-digital converter, the stability, is evaluated through a lengthy temperature-voltage testing over the range of -40C to +85C and voltage variations of +/- 10% of the nominal supply voltage. The stability of both the bitstrings and the underlying physical parameters is evaluated for the PUFs using the data collected from the hardware experiments and supported with software simulations conducted on the devices. Several novel techniques are proposed and successfully tested that address known issues related to instability of PUFs to changing temperature and voltage conditions, thus rendering our PUFs more resilient to these changing conditions faced in practical use. Lastly, an analysis of the stability to changing temperature and voltage variations of a third PUF that leverages random variations in the resistance of the metal wires in the power and ground grids of a chip is also presented

    Comprehensive study of physical unclonable functions on FPGAs: correlation driven Implementation, deep learning modeling attacks, and countermeasures

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    For more than a decade and a half, Physical Unclonable Functions (PUFs) have been presented as a promising hardware security primitive. The idea of exploiting variabilities in hardware fabrication to generate a unique fingerprint for every silicon chip introduced a more secure and cheaper alternative. Other solutions using non-volatile memory to store cryptographic keys, require additional processing steps to generate keys externally, and secure environments to exchange generated keys, which introduce many points of attack that can be used to extract the secret keys. PUFs were addressed in the literature from different perspectives. Many publications focused on proposing new PUF architectures and evaluation metrics to improve security properties like response uniqueness per chip, response reproducibility of the same PUF input, and response unpredictability using previous input/response pairs. Other research proposed attack schemes to clone the response of PUFs, using conventional machine learning (ML) algorithms, side-channel attacks using power and electromagnetic traces, and fault injection using laser beams and electromagnetic pulses. However, most attack schemes to be successful, imposed some restrictions on the targeted PUF architectures, which make it simpler and easier to attack. Furthermore, they did not propose solid and provable enhancements on these architectures to countermeasure the attacks. This leaves many open questions concerning how to implement perfect secure PUFs especially on FPGAs, how to extend previous modeling attack schemes to be successful against more complex PUF architectures (and understand why modeling attacks work) and how to detect and countermeasure these attacks to guarantee that secret data are safe from the attackers. This Ph.D. dissertation contributes to the state of the art research on physical unclonable functions in several ways. First, the thesis provides a comprehensive analysis of the implementation of secure PUFs on FPGAs using manual placement and manual routing techniques guided by new performance metrics to overcome FPGAs restrictions with minimum hardware and area overhead. Then the impact of deep learning (DL) algorithms is studied as a promising modeling attack scheme against complex PUF architectures, which were reported immune to conventional (ML) techniques. Furthermore, it is shown that DL modeling attacks successfully overcome the restrictions imposed by previous research even with the lack of accurate mathematical models of these PUF architectures. Finally, this comprehensive analysis is completed by understanding why deep learning attacks are successful and how to build new PUF architectures and extra circuitry to thwart these types of attacks. This research is important for deploying cheap and efficient hardware security primitives in different fields, including IoT applications, embedded systems, automotive and military equipment. Additionally, it puts more focus on the development of strong intrinsic PUFs which are widely proposed and deployed in many security protocols used for authentication, key establishment, and Oblivious transfer protocols

    ICR ANNUAL REPORT 2019 (Volume 26)[All Pages]

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    This Annual Report covers from 1 January to 31 December 201

    Smart Wireless Sensor Networks

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    The recent development of communication and sensor technology results in the growth of a new attractive and challenging area - wireless sensor networks (WSNs). A wireless sensor network which consists of a large number of sensor nodes is deployed in environmental fields to serve various applications. Facilitated with the ability of wireless communication and intelligent computation, these nodes become smart sensors which do not only perceive ambient physical parameters but also be able to process information, cooperate with each other and self-organize into the network. These new features assist the sensor nodes as well as the network to operate more efficiently in terms of both data acquisition and energy consumption. Special purposes of the applications require design and operation of WSNs different from conventional networks such as the internet. The network design must take into account of the objectives of specific applications. The nature of deployed environment must be considered. The limited of sensor nodes� resources such as memory, computational ability, communication bandwidth and energy source are the challenges in network design. A smart wireless sensor network must be able to deal with these constraints as well as to guarantee the connectivity, coverage, reliability and security of network's operation for a maximized lifetime. This book discusses various aspects of designing such smart wireless sensor networks. Main topics includes: design methodologies, network protocols and algorithms, quality of service management, coverage optimization, time synchronization and security techniques for sensor networks

    Building on Progress - Expanding the Research Infrastructure for the Social, Economic, and Behavioral Sciences. Vol. 1

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    The publication provides a comprehensive compendium of the current state of Germany's research infrastructure in the social, economic, and behavioural sciences. In addition, the book presents detailed discussions of the current needs of empirical researchers in these fields and opportunities for future development. The book contains 68 advisory reports by more than 100 internationally recognized authors from a wide range of fields and recommendations by the German Data Forum (RatSWD) on how to improve the research infrastructure so as to create conditions ideal for making Germany's social, economic, and behavioral sciences more innovative and internationally competitive. The German Data Forum (RatSWD) has discussed the broad spectrum of issues covered by these advisory reports extensively, and has developed general recommendations on how to expand the research infrastructure to meet the needs of scholars in the social and economic sciences
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