Preprint: Using RF-DNA Fingerprints To Classify OFDM Transmitters Under Rayleigh Fading Conditions

The Internet of Things (IoT) is a collection of Internet connected devices capable of interacting with the physical world and computer systems. It is estimated that the IoT will consist of approximately fifty billion devices by the year 2020. In addition to the sheer numbers, the need for IoT security is exacerbated by the fact that many of the edge devices employ weak to no encryption of the communication link. It has been estimated that almost 70% of IoT devices use no form of encryption. Previous research has suggested the use of Specific Emitter Identification (SEI), a physical layer technique, as a means of augmenting bit-level security mechanism such as encryption. The work presented here integrates a Nelder-Mead based approach for estimating the Rayleigh fading channel coefficients prior to the SEI approach known as RF-DNA fingerprinting. The performance of this estimator is assessed for degrading signal-to-noise ratio and compared with least square and minimum mean squared error channel estimators. Additionally, this work presents classification results using RF-DNA fingerprints that were extracted from received signals that have undergone Rayleigh fading channel correction using Minimum Mean Squared Error (MMSE) equalization. This work also performs radio discrimination using RF-DNA fingerprints generated from the normalized magnitude-squared and phase response of Gabor coefficients as well as two classifiers. Discrimination of four 802.11a Wi-Fi radios achieves an average percent correct classification of 90% or better for signal-to-noise ratios of 18 and 21 dB or greater using a Rayleigh fading channel comprised of two and five paths, respectively.Comment: 13 pages, 14 total figures/images, Currently under review by the IEEE Transactions on Information Forensics and Securit

Biometric Systems

Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study

Proceedings of the 35th WIC Symposium on Information Theory in the Benelux and the 4th joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux, Eindhoven, the Netherlands May 12-13, 2014

Compressive sensing (CS) as an approach for data acquisition has recently received much attention. In CS, the signal recovery problem from the observed data requires the solution of a sparse vector from an underdetermined system of equations. The underlying sparse signal recovery problem is quite general with many applications and is the focus of this talk. The main emphasis will be on Bayesian approaches for sparse signal recovery. We will examine sparse priors such as the super-Gaussian and student-t priors and appropriate MAP estimation methods. In particular, re-weighted l2 and re-weighted l1 methods developed to solve the optimization problem will be discussed. The talk will also examine a hierarchical Bayesian framework and then study in detail an empirical Bayesian method, the Sparse Bayesian Learning (SBL) method. If time permits, we will also discuss Bayesian methods for sparse recovery problems with structure; Intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector problem

Proceedings of the 35th WIC Symposium on Information Theory in the Benelux and the 4th joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux, Eindhoven, the Netherlands May 12-13, 2014

Compressive sensing (CS) as an approach for data acquisition has recently received much attention. In CS, the signal recovery problem from the observed data requires the solution of a sparse vector from an underdetermined system of equations. The underlying sparse signal recovery problem is quite general with many applications and is the focus of this talk. The main emphasis will be on Bayesian approaches for sparse signal recovery. We will examine sparse priors such as the super-Gaussian and student-t priors and appropriate MAP estimation methods. In particular, re-weighted l2 and re-weighted l1 methods developed to solve the optimization problem will be discussed. The talk will also examine a hierarchical Bayesian framework and then study in detail an empirical Bayesian method, the Sparse Bayesian Learning (SBL) method. If time permits, we will also discuss Bayesian methods for sparse recovery problems with structure; Intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector problem

Circuit Techniques for Low-Power and Secure Internet-of-Things Systems

The coming of Internet of Things (IoT) is expected to connect the physical world to the cyber world through ubiquitous sensors, actuators and computers. The nature of these applications demand long battery life and strong data security. To connect billions of things in the world, the hardware platform for IoT systems must be optimized towards low power consumption, high energy efficiency and low cost. With these constraints, the security of IoT systems become a even more difficult problem compared to that of computer systems. A new holistic system design considering both hardware and software implementations is demanded to face these new challenges. In this work, highly robust and low-cost true random number generators (TRNGs) and physically unclonable functions (PUFs) are designed and implemented as security primitives for secret key management in IoT systems. They provide three critical functions for crypto systems including runtime secret key generation, secure key storage and lightweight device authentication. To achieve robustness and simplicity, the concept of frequency collapse in multi-mode oscillator is proposed, which can effectively amplify the desired random variable in CMOS devices (i.e. process variation or noise) and provide a runtime monitor of the output quality. A TRNG with self-tuning loop to achieve robust operation across -40 to 120 degree Celsius and 0.6 to 1V variations, a TRNG that can be fully synthesized with only standard cells and commercial placement and routing tools, and a PUF with runtime filtering to achieve robust authentication, are designed based upon this concept and verified in several CMOS technology nodes. In addition, a 2-transistor sub-threshold amplifier based "weak" PUF is also presented for chip identification and key storage. This PUF achieves state-of-the-art 1.65% native unstable bit, 1.5fJ per bit energy efficiency, and 3.16% flipping bits across -40 to 120 degree Celsius range at the same time, while occupying only 553 feature size square area in 180nm CMOS. Secondly, the potential security threats of hardware Trojan is investigated and a new Trojan attack using analog behavior of digital processors is proposed as the first stealthy and controllable fabrication-time hardware attack. Hardware Trojan is an emerging concern about globalization of semiconductor supply chain, which can result in catastrophic attacks that are extremely difficult to find and protect against. Hardware Trojans proposed in previous works are based on either design-time code injection to hardware description language or fabrication-time modification of processing steps. There have been defenses developed for both types of attacks. A third type of attack that combines the benefits of logical stealthy and controllability in design-time attacks and physical "invisibility" is proposed in this work that crosses the analog and digital domains. The attack eludes activation by a diverse set of benchmarks and evades known defenses. Lastly, in addition to security-related circuits, physical sensors are also studied as fundamental building blocks of IoT systems in this work. Temperature sensing is one of the most desired functions for a wide range of IoT applications. A sub-threshold oscillator based digital temperature sensor utilizing the exponential temperature dependence of sub-threshold current is proposed and implemented. In 180nm CMOS, it achieves 0.22/0.19K inaccuracy and 73mK noise-limited resolution with only 8865 square micrometer additional area and 75nW extra power consumption to an existing IoT system.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138779/1/kaiyuan_1.pd

D2.1 - Report on Selected TRNG and PUF Principles

This report represents the final version of Deliverable 2.1 of the HECTOR work package WP2. It is a result of discussions and work on Task 2.1 of all HECTOR partners involved in WP2. The aim of the Deliverable 2.1 is to select principles of random number generators (RNGs) and physical unclonable functions (PUFs) that fulfill strict technology, design and security criteria. For example, the selected RNGs must be suitable for implementation in logic devices according to the German AIS20/31 standard. Correspondingly, the selected PUFs must be suitable for applying similar security approach. A standard PUF evaluation approach does not exist, yet, but it should be proposed in the framework of the project. Selected RNGs and PUFs should be then thoroughly evaluated from the point of view of security and the most suitable principles should be implemented in logic devices, such as Field Programmable Logic Arrays (FPGAs) and Application Specific Integrated Circuits (ASICs) during the next phases of the project