Exploitation of RF-DNA for Device Classification and Verification Using GRLVQI Processing

This dissertation introduces a GRLVQI classifier into an RF-DNA fingerprinting process and demonstrates applicability for device classification and ID verification. Unlike MDA/ML processing, GRLVQI provides a measure of feature relevance that enables Dimensional Reduction Analysis (DRA) to enhance the experimental-to-operational transition potential of RF-DNA fingerprinting. Using 2D Gabor Transform RF-DNA fingerprints extracted from experimentally collected OFDM-based 802.16 WiMAX and 802.11 WiFi device emissions, average GRLVQI classification accuracy of %C greater than or equal to 90% is achieved using full and reduced dimensional feature sets at SNR greater than or equal to 10.0 dB and SNR greater than or equal to 12.0 dB, respectively. Performance with DRA approximately 90% reduced feature sets included %C greater than or equal to 90% for 1) WiMAX features at SNR greater than or equal to 12.0 dB and 2) WiFi features at SNR greater than or equal to 13.0 dB. For device ID verification with DRA approximately 90% feature sets, GRLVQI enabled: 1) 100% ID verification of authorized WiMAX devices and 97% detection of spoofing attacks by rogue devices at SNR=18.0 dB, and 2) 100% ID verification of authorized WiFi devices at SNR=15.0 dB

Learning from Power Signals: An Automated Approach to Electrical Disturbance Identification Within a Power Transmission System

As power quality becomes a higher priority in the electric utility industry, the amount of disturbance event data continues to grow. Utilities do not have the required personnel to analyze each event by hand. This work presents an automated approach for analyzing power quality events recorded by digital fault recorders and power quality monitors operating within a power transmission system. The automated approach leverages rule-based analytics to examine the time and frequency domain characteristics of the voltage and current signals. Customizable thresholds are set to categorize each disturbance event. The events analyzed within this work include various faults, motor starting, and incipient instrument transformer failure. Analytics for fourteen different event types have been developed. The analytics were tested on 160 signal files and yielded an accuracy of ninety-nine percent. Continuous, nominal signal data analysis is performed using an approach coined as the cyclic histogram. The cyclic histogram process will be integrated into the digital fault recorders themselves to facilitate the detection of subtle signal variations that are too small to trigger a disturbance event and that can occur over hours or days. In addition to reducing memory requirements by a factor of 320, it is anticipated that cyclic histogram processing will aid in identifying incipient events and identifiers. This project is expected to save engineers time by automating the classification of disturbance events and increase the reliability of the transmission system by providing near real time detection and identification of disturbances as well as prevention of problems before they occur.Comment: 18 page

Considerations, Advances, and Challenges Associated with the Use of Specific Emitter Identification in the Security of Internet of Things Deployments: A Survey

Initially introduced almost thirty years ago for the express purpose of providing electronic warfare systems the capabilities to detect, characterize, and identify radar emitters, Specific Emitter Identification (SEI) has recently received a lot of attention within the research community as a physical layer technique for securing Internet of Things (IoT) deployments. This attention is largely due to SEI’s demonstrated success in passively and uniquely identifying wireless emitters using traditional machine learning and the success of Deep Learning (DL) within the natural language processing and computer vision areas. SEI exploits distinct and unintentional features present within an emitter’s transmitted signals. These distinctive and unintentional features are attributed to slight manufacturing and assembly variations within and between the components, sub-systems, and systems comprising an emitter’s Radio Frequency (RF) front end. Although sufficient to facilitate SEI, these features do not hinder normal operations such as detection, channel estimation, timing, and demodulation. However, despite the plethora of SEI publications, it has remained largely a focus of academic endeavors, primarily focusing on proof-of-concept demonstration and little to no use in operational networks for various reasons. The focus of this survey is a review of SEI publications from the perspective of its use as a practical, effective, and usable IoT security mechanism; thus, we use IoT requirements and constraints (e.g., wireless standard, nature of their deployment) as a lens through which each reviewed paper is analyzed. Previous surveys have not taken such an approach and have only used IoT as motivation, a setting, or a context. In this survey, we consider operating conditions, SEI threats, SEI at scale, publicly available data sets, and SEI considerations that are dictated by the fact that it is to be employed by IoT devices or IoT infrastructure

An investigation into the impacts of deep learning‐based re‐sampling on specific emitter identification performance

Abstract Increasing Internet of Things (IoT) deployments present a growing surface over which villainous actors can carry out attacks. This disturbing revelation is amplified by the fact that most IoT devices use weak or no encryption. Specific Emitter Identification (SEI) is an approach intended to address this IoT security weakness. This work provides the first Deep Learning (DL) driven SEI approach that upsamples the signals after collection to improve performance while reducing the hardware requirements of the IoT devices that collect them. DL‐driven upsampling results in superior SEI performance versus two traditional upsampling approaches and a convolutional neural network‐only approach

Physical Layer-Based IoT Security: An Investigation Into Improving Preamble-Based SEI Performance When Using Multiple Waveform Collections

The Internet of Things (IoT) is a collection of inexpensive, semi-autonomous, Internet-connected devices that sense and interact within the physical world. IoT security is of paramount concern because most IoT devices use weak or no encryption at all. This concern is exacerbated by the fact that the number of IoT deployments continues to grow, IoT devices are being integrated into key infrastructures, and their weak or lack of encryption is being exploited. Specific Emitter Identification (SEI) is being investigated as an effective, cost-saving IoT security approach because it is a passive technique that uses inherent, distinct features that are unintentionally imparted to the waveform during its formation and transmission by the IoT device’s Radio Frequency (RF) front-end. Despite the amount of research conducted, SEI still faces roadblocks that hinder its integration within operational networks. Our work focuses on the lack of feature permanence across time and environments, which is designated herein as the “multi-day” problem. We present results and analysis for six distinct experiments focused on improving multi-day SEI performance through multiple waveform representations, deeper Convolutional Neural Networks (CNNs), increasing numbers of waveforms, channel model impacts, and two-channel mitigation techniques. Our work shows improved multi-day SEI performance using the waveform’s frequency-domain representation and a CNN comprised of four convolutional layers. However, the traditional channel model and both channel mitigation techniques fail to sufficiently mitigate or remove real-world channel impacts, which suggests that the channel may not be the dominant effect hindering multi-day SEI performance