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Data to hardware binding with physical unclonable functions
The various technologies presented herein relate to binding data (e.g., software) to hardware, wherein the hardware is to utilize the data. The generated binding can be utilized to detect whether at least one of the hardware or the data has been modified between an initial moment (enrollment) and a later moment (authentication). During enrollment, an enrollment value is generated that includes a signature of the data, a first response from a PUF located on the hardware, and a code word. During authentication, a second response from the PUF is utilized to authenticate any of the content in the enrollment value, and based upon the authentication, a determination can be made regarding whether the hardware and/or the data have been modified. If modification is detected then a mitigating operation can be performed, e.g., the hardware is prevented from utilizing the data. If no modification is detected, the data can be utilized.
Physical Unclonable Functions (PUFs) for Securing the Internet of Things (IoT).
Abstract not provided.
Computational optical physical unclonable function
A system or method for encryption of data includes a light source, a random optical element and a light detection element. The light source is arranged to transmit an input data signal to the random optical element. The light source is incident on the random optical element such that the input data signal is randomly scattered by the random optical element to generate an image at on the detector disposed at an output of the random optical element. The image received by the detector is applied to a compressive sensing algorithm to generate a transfer function. The transfer function defines a relationship between the input data signal and the image to enable estimation and reconstruction of the input data signal.
Detecting Changes to Logic Configuration with Physical Unclonable Functions in FPGAs.
Abstract not provided.
A Physically Unclonable Function (PUF) Using Micromagnets and NV Magnetic Imaging.
Abstract not provided.
A physically unclonable function using micron-scale magnetic structures and NV diamond magnetic sensing.
Abstract not provided.
Computational optical physical unclonable function
A system or method for encryption of data includes a light source, a random optical element and a light detection element. The light source is arranged to transmit an input data signal to the random optical element. The light source is incident on the random optical element such that the input data signal is randomly scattered by the random optical element to generate an image at on the detector disposed at an output of the random optical element. The image received by the detector is applied to a compressive sensing algorithm to generate a transfer function. The transfer function defines a relationship between the input data signal and the image to enable estimation and reconstruction of the input data signal.
Physical Layer Entropy Analysis for Physical Unclonable Functions
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R&D100: IC ID
Supply chain security to detect, deter, and prevent the counterfeiting of networked and stand-alone integrated circuits (ICs) is critical to cyber security. Sandia National Laboratory researchers have developed IC ID to leverage Physically Unclonable Functions (PUFs) and strong cryptographic authentication to create a unique fingerprint for each integrated circuit. IC ID assures the authenticity of ICs to prevent tampering or malicious substitution.
PUF-Based Two-Factor Authentication Protocol for Securing the Power Grid Against Insider Threat
Recent advances in smart grid technologies have enabled additional distributed control paradigms that allow more efficient and reliable operation. However, this creates new security concerns for the grid, such as attackers using spoofed grid control devices to generate false measurements. This paper introduces a two-factor authentication protocol leveraging standard public-key cryptography as one authentication factor and a hardware-based fingerprint, known as a Physical Unclonable Function, as a second authentication factor. This protocol incurs a small overhead and prevents cyber-attacks even when an adversary is able to compromise the cryptographic keys stored in the non-volatile memory of an intelligent control device.
Predictor-corrector models for lightweight massive machine-type communications in Industry 4.0
Future Industry 4.0 scenarios are characterized by seamless integration between computational and physical processes. To achieve this objective, dense platforms made of small sensing nodes and other resource constraint devices are ubiquitously deployed. All these devices have a limited number of computational resources, just enough to perform the simple operation they are in charge of. The remaining operations are delegated to powerful gateways that manage sensing nodes, but resources are never unlimited, and as more and more devices are deployed on Industry 4.0 platforms, gateways present more problems to handle massive machine-type communications. Although the problems are diverse, those related to security are especially critical. To enable sensing nodes to establish secure communications, several semiconductor companies are currently promoting a new generation of devices based on Physical Unclonable Functions, whose usage grows every year in many real industrial scenarios. Those hardware devices do not consume any computational resource but force the gateway to keep large key-value catalogues for each individual node. In this context, memory usage is not scalable and processing delays increase exponentially with each new node on the platform. In this paper, we address this challenge through predictor-corrector models, representing the key-value catalogues. Models are mathematically complex, but we argue that they consume less computational resources than current approaches. The lightweight models are based on complex functions managed as Laurent series, cubic spline interpolations, and Boolean functions also developed as series. Unknown parameters in these models are predicted, and eventually corrected to calculate the output value for each given key. The initial parameters are based on the Kane Yee formula. An experimental analysis and a performance evaluation are provided in the experimental section, showing that the proposed approach causes a significant reduction in the resource consumption.
Use of Thermistor Temperature Sensors for Cyber-Physical System Security
The last few decades have seen a large proliferation in the prevalence of cyber-physical systems. This has been especially highlighted by the explosive growth in the number of Internet of Things (IoT) devices. Unfortunately, the increasing prevalence of these devices has begun to draw the attention of malicious entities which exploit them for their own gain. What makes these devices especially attractive is the various resource constraints present in these devices that make it difficult to add standard security features. Therefore, one intriguing research direction is creating security solutions out of already present components such as sensors. Physically Unclonable Functions (PUFs) are one potential solution that use intrinsic variations of the device manufacturing process for provisioning security. In this work, we propose a novel weak PUF design using thermistor temperature sensors. Our design uses the differences in resistance variation between thermistors in response to temperature change. To generate a PUF that is reliable across a range of temperatures, we use a response-generation algorithm that helps mitigate the effects of temperature variation on the thermistors. We tested the performance of our proposed design across a range of environmental operating conditions. From this we were able to evaluate the reliability of the proposed PUF with respect to variations in temperature and humidity. We also evaluated the PUF’s uniqueness using Monte Carlo simulations.