Non-invasive Techniques Towards Recovering Highly Secure Unclonable Cryptographic Keys and Detecting Counterfeit Memory Chips

Due to the ubiquitous presence of memory components in all electronic computing systems, memory-based signatures are considered low-cost alternatives to generate unique device identifiers (IDs) and cryptographic keys. On the one hand, this unique device ID can potentially be used to identify major types of device counterfeitings such as remarked, overproduced, and cloned. On the other hand, memory-based cryptographic keys are commercially used in many cryptographic applications such as securing software IP, encrypting key vault, anchoring device root of trust, and device authentication for could services. As memory components generate this signature in runtime rather than storing them in memory, an attacker cannot clone/copy the signature and reuse them in malicious activity. However, to ensure the desired level of security, signatures generated from two different memory chips should be completely random and uncorrelated from each other. Traditionally, memory-based signatures are considered unique and uncorrelated due to the random variation in the manufacturing process. Unfortunately, in previous studies, many deterministic components of the manufacturing process, such as memory architecture, layout, systematic process variation, device package, are ignored. This dissertation shows that these deterministic factors can significantly correlate two memory signatures if those two memory chips share the same manufacturing resources (i.e., manufacturing facility, specification set, design file, etc.). We demonstrate that this signature correlation can be used to detect major counterfeit types in a non-invasive and low-cost manner. Furthermore, we use this signature correlation as side-channel information to attack memory-based cryptographic keys. We validate our contribution by collecting data from several commercially available off-the-shelf (COTS) memory chips/modules and considering different usage-case scenarios

Semiconductor Memory Applications in Radiation Environment, Hardware Security and Machine Learning System

abstract: Semiconductor memory is a key component of the computing systems. Beyond the conventional memory and data storage applications, in this dissertation, both mainstream and eNVM memory technologies are explored for radiation environment, hardware security system and machine learning applications. In the radiation environment, e.g. aerospace, the memory devices face different energetic particles. The strike of these energetic particles can generate electron-hole pairs (directly or indirectly) as they pass through the semiconductor device, resulting in photo-induced current, and may change the memory state. First, the trend of radiation effects of the mainstream memory technologies with technology node scaling is reviewed. Then, single event effects of the oxide based resistive switching random memory (RRAM), one of eNVM technologies, is investigated from the circuit-level to the system level. Physical Unclonable Function (PUF) has been widely investigated as a promising hardware security primitive, which employs the inherent randomness in a physical system (e.g. the intrinsic semiconductor manufacturing variability). In the dissertation, two RRAM-based PUF implementations are proposed for cryptographic key generation (weak PUF) and device authentication (strong PUF), respectively. The performance of the RRAM PUFs are evaluated with experiment and simulation. The impact of non-ideal circuit effects on the performance of the PUFs is also investigated and optimization strategies are proposed to solve the non-ideal effects. Besides, the security resistance against modeling and machine learning attacks is analyzed as well. Deep neural networks (DNNs) have shown remarkable improvements in various intelligent applications such as image classification, speech classification and object localization and detection. Increasing efforts have been devoted to develop hardware accelerators. In this dissertation, two types of compute-in-memory (CIM) based hardware accelerator designs with SRAM and eNVM technologies are proposed for two binary neural networks, i.e. hybrid BNN (HBNN) and XNOR-BNN, respectively, which are explored for the hardware resource-limited platforms, e.g. edge devices.. These designs feature with high the throughput, scalability, low latency and high energy efficiency. Finally, we have successfully taped-out and validated the proposed designs with SRAM technology in TSMC 65 nm. Overall, this dissertation paves the paths for memory technologies’ new applications towards the secure and energy-efficient artificial intelligence system.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

MEMS sensors as physical unclonable functions

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

Hardware-software co-design of an iris recognition algorithm

This paper describes the implementation of an iris recognition algorithm based on hardware-software co-design. The system architecture consists of a general-purpose 32- bit microprocessor and several slave coprocessors that accelerate the most intensive calculations. The whole iris recognition algorithm has been implemented on a low-cost Spartan 3 FPGA, achieving significant reduction in execution time when compared to a conventional software-based application. Experimental results show that with a clock speed of 40 MHz, an IrisCode is obtained in less than 523 ms from an image of 640x480 pixels, which is just 20% of the total time needed by a software solution running on the same microprocessor embedded in the architecture.Peer ReviewedPreprin

Segurança de computadores por meio de autenticação intrínseca de hardware

Orientadores: Guido Costa Souza de Araújo, Mario Lúcio Côrtes e Diego de Freitas AranhaTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Neste trabalho apresentamos Computer Security by Hardware-Intrinsic Authentication (CSHIA), uma arquitetura de computadores segura para sistemas embarcados que tem como objetivo prover autenticidade e integridade para código e dados. Este trabalho está divido em três fases: Projeto da Arquitetura, sua Implementação, e sua Avaliação de Segurança. Durante a fase de projeto, determinamos como integridade e autenticidade seriam garantidas através do uso de Funções Fisicamente Não Clonáveis (PUFs) e propusemos um algoritmo de extração de chaves criptográficas de memórias cache de processadores. Durante a implementação, flexibilizamos o projeto da arquitetura para fornecer diferentes possibilidades de configurações sem comprometimento da segurança. Então, avaliamos seu desempenho levando em consideração o incremento em área de chip, aumento de consumo de energia e memória adicional para diferentes configurações. Por fim, analisamos a segurança de PUFs e desenvolvemos um novo ataque de canal lateral que circunvê a propriedade de unicidade de PUFs por meio de seus elementos de construçãoAbstract: This work presents Computer Security by Hardware-Intrinsic Authentication (CSHIA), a secure computer architecture for embedded systems that aims at providing authenticity and integrity for code and data. The work encompassed three phases: Design, Implementation, and Security Evaluation. In design, we laid out the basic ideas behind CSHIA, namely, how integrity and authenticity are employed through the use of Physical Unclonable Functions (PUFs), and we proposed an algorithm to extract cryptographic keys from the intrinsic memories of processors. In implementation, we made CSHIA¿s design more flexible, allowing different configurations without compromising security. Then, we evaluated CSHIA¿s performance and overheads, such as area, energy, and memory, for multiple configurations. Finally, we evaluated security of PUFs, which led us to develop a new side-channel-based attack that enabled us to circumvent PUFs¿ uniqueness property through their architectural elementsDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação2015/06829-2; 2016/25532-3147614/2014-7FAPESPCNP

Trusted Cameras on Mobile Devices Based on SRAM Physically Unclonable Functions

Nowadays, there is an increasing number of cameras placed on mobile devices connected to the Internet. Since these cameras acquire and process sensitive and vulnerable data in applications such as surveillance or monitoring, security is essential to avoid cyberattacks. However, cameras on mobile devices have constraints in size, computation and power consumption, so that lightweight security techniques should be considered. Camera identification techniques guarantee the origin of the data. Among the camera identification techniques, Physically Unclonable Functions (PUFs) allow generating unique, distinctive and unpredictable identifiers from the hardware of a device. PUFs are also very suitable to obfuscate secret keys (by binding them to the hardware of the device) and generate random sequences (employed as nonces). In this work, we propose a trusted camera based on PUFs and standard cryptographic algorithms. In addition, a protocol is proposed to protect the communication with the trusted camera, which satisfies authentication, confidentiality, integrity and freshness in the data communication. This is particularly interesting to carry out camera control actions and firmware updates. PUFs from Static Random Access Memories (SRAMs) are selected because cameras typically include SRAMs in its hardware. Therefore, additional hardware is not required and security techniques can be implemented at low cost. Experimental results are shown to prove how the proposed solution can be implemented with the SRAM of commercial Bluetooth Low Energy (BLE) chips included in the communication module of the camera. A proof of concept shows that the proposed solution can be implemented in low-cost cameras.España, Ministerio de Ciencia e Innovación TEC2014-57971-R TEC2017-83557-