Tweak: Towards Portable Deep Learning Models for Domain-Agnostic LoRa Device Authentication

Deep learning based device fingerprinting has emerged as a key method of identifying and authenticating devices solely via their captured RF transmissions. Conventional approaches are not portable to different domains in that if a model is trained on data from one domain, it will not perform well on data from a different but related domain. Examples of such domains include the receiver hardware used for collecting the data, the day/time on which data was captured, and the protocol configuration of devices. This work proposes Tweak, a technique that, using metric learning and a calibration process, enables a model trained with data from one domain to perform well on data from another domain. This process is accomplished with only a small amount of training data from the target domain and without changing the weights of the model, which makes the technique computationally lightweight and thus suitable for resource-limited IoT networks. This work evaluates the effectiveness of Tweak vis-a-vis its ability to identify IoT devices using a testbed of real LoRa-enabled devices under various scenarios. The results of this evaluation show that Tweak is viable and especially useful for networks with limited computational resources and applications with time-sensitive missions

Hardware and software fingerprinting of mobile devices

This dissertation presents novel and practical algorithms to identify the software and hardware components on mobile devices. In particular, we make significant contributions in two challenging areas: library fingerprinting, to identify third-party software libraries, and device fingerprinting, to identify individual hardware components. Our work has significant implications for the privacy and security of mobile platforms. Software-based library fingerprinting can be used to detect vulnerable libraries and uncover large-scale data collection activities. We develop a novel Android library finger-printing tool, LibID, to reliably identify specific versions of in-app third-party libraries. LibID is more effective against code obfuscation than prior art. When comparing LibID with other tools in identifying the correct library version using obfuscated F-Droid apps, LibID achieves an F1 score of more than 0.5 in all cases while prior work is below 0.25. We also demonstrate the utility of LibID by detecting the use of a vulnerable version of the OkHttp library in nearly 10% of the 3 958 popular apps on the Google Play Store. Hardware-based device fingerprinting allows apps and websites to invade user privacy by tracking user activity online as the user moves between apps or websites. In particular, we present a new type of device fingerprinting attack, the factory calibration fingerprinting attack, that recovers embedded per-device factory calibration data from motion sensors in a smartphone. We investigate the calibration behaviour of each sensor and show that the calibration fingerprint is fast to generate, does not change over time or after a factory reset, and can be obtained without any special user permissions. We estimate the entropy of the calibration fingerprint and find the fingerprint is very likely to be globally unique for iOS devices (~67 bits of entropy for iPhone 6S) and recent Google Pixel devices (~57 bits of entropy for Pixel 4/4 XL). By comparison, the fingerprint generated by previous work has at most 13 bits of entropy. Following our disclosures, Apple deployed a fix in iOS 12.2 and Google in Android 11. Both code obfuscation and factory calibration help to hide software and hardware idiosyncrasies from third-parties, but this dissertation demonstrates that reliable software and hardware fingerprints can still be generated given sufficient knowledge and a suitable approach. Our work has significant practical implications and can be used to improve platform security and protect user privacy.China Scholarship Council The Boeing Company Microsoft Researc

Global Shipping Container Monitoring Using Machine Learning with Multi-Sensor Hubs and Catadioptric Imaging

We describe a framework for global shipping container monitoring using machine learning with multi-sensor hubs and infrared catadioptric imaging. A wireless mesh radio satellite tag architecture provides connectivity anywhere in the world which is a significant improvement to legacy methods. We discuss the design and testing of a low-cost long-wave infrared catadioptric imaging device and multi-sensor hub combination as an intelligent edge computing system that, when equipped with physics-based machine learning algorithms, can interpret the scene inside a shipping container to make efficient use of expensive communications bandwidth. The histogram of oriented gradients and T-channel (HOG+) feature as introduced for human detection on low-resolution infrared catadioptric images is shown to be effective for various mirror shapes designed to give wide volume coverage with controlled distortion. Initial results for through-metal communication with ultrasonic guided waves show promise using the Dynamic Wavelet Fingerprint Technique (DWFT) to identify Lamb waves in a complicated ultrasonic signal

MAC layer assisted localization in wireless environments with multiple sensors and multiple emitters

Extreme emitter density (EED) RF environments, defined as 10k-100k emitters within a footprint of less than 1 km squared, are becoming increasingly common with the proliferation of personal devices containing myriad communication standards (e.g. WLAN, Bluetooth, 4G, etc). Attendees at concerts, sporting events, and other such large-scale events desire to be connected at all times, creating tremendous spectrum management challenges, especially in unlicensed frequencies such as 2.4 GHz, 5 GHz, or 900 MHz Industrial, Scientific, and Medical (ISM) bands. In licensed bands, there are often critical communication systems such as two-way radios for emergency personnel which must be free from interference. Identification and localization of a non-conforming or interfering Emitter of Interest (EoI) is important for these critical systems. In this dissertation, research is conducted to improve localization for these EED RF environments by exploiting side information available at the Medium Access Control (MAC) layer. The primary contributions of this research are: (1) A testbed in Bobby Dodd football stadium consisting of three spatially distributed, time-synchronized RF Sensor Nodes (RFSN) collecting and archiving complex baseband samples for algorithm development and validation. (2) A modeling framework and analytical results on the benefits of exploiting the structure of the MAC layer for associating physical layer measurements, such as Time Difference of Arrivals (TDoA), to emitters. (3) A three stage localization algorithm exploiting time between packets and a constrained geometry to shrink the error ellipse of the emitter position estimate. The results are expected to improve localization accuracy in wireless environments when multiple sensors observe multiple emitters using a known communications protocol within a constrained geometry.Ph.D

Biosensors for Diagnosis and Monitoring

Biosensor technologies have received a great amount of interest in recent decades, and this has especially been the case in recent years due to the health alert caused by the COVID-19 pandemic. The sensor platform market has grown in recent decades, and the COVID-19 outbreak has led to an increase in the demand for home diagnostics and point-of-care systems. With the evolution of biosensor technology towards portable platforms with a lower cost on-site analysis and a rapid selective and sensitive response, a larger market has opened up for this technology. The evolution of biosensor systems has the opportunity to change classic analysis towards real-time and in situ detection systems, with platforms such as point-of-care and wearables as well as implantable sensors to decentralize chemical and biological analysis, thus reducing industrial and medical costs. This book is dedicated to all the research related to biosensor technologies. Reviews, perspective articles, and research articles in different biosensing areas such as wearable sensors, point-of-care platforms, and pathogen detection for biomedical applications as well as environmental monitoring will introduce the reader to these relevant topics. This book is aimed at scientists and professionals working in the field of biosensors and also provides essential knowledge for students who want to enter the field

Secure OFDM System Design for Wireless Communications

Wireless communications is widely employed in modern society and plays an increasingly important role in people\u27s daily life. The broadcast nature of radio propagation, however, causes wireless communications particularly vulnerable to malicious attacks, and leads to critical challenges in securing the wireless transmission. Motivated by the insufficiency of traditional approaches to secure wireless communications, physical layer security that is emerging as a complement to the traditional upper-layer security mechanisms is investigated in this dissertation. Five novel techniques toward the physical layer security of wireless communications are proposed. The first two techniques focus on the security risk assessment in wireless networks to enable a situation-awareness based transmission protection. The third and fourth techniques utilize wireless medium characteristics to enhance the built-in security of wireless communication systems, so as to prevent passive eavesdropping. The last technique provides an embedded confidential signaling link for secure transmitter-receiver interaction in OFDM systems

Standoff Sensing Technology Based on Laser-Induced Breakdown Spectroscopy: Advanced Targeting, Surveillance and Reconnaissance in Security and Architectural Heritage Applications

Due to the ability to perform simultaneous, multi-element and real-time analysis without pretreatment and doing from a distance, laser induced breakdown spectroscopy (LIBS) in standoff mode is now considered a cutting-edge analytical technology. All these features have allowed its application in various fields such as security, environment, cultural heritage protection and space exploration, among the more outstanding. Nonetheless, the fact of working to long distances involves greater difficulties than in a lab-scale. Thus, in a first part of this memory, the behavior of the analytical signal has been assessed. On the other hand, a second part demonstrates the applicability of the technique in standoff mode for solving real-life problems. • Fundamental studies 1. Main causes affecting the uncertainty of the analytical signal in standoff LIBS. One of the most sensitive issues in standoff LIBS is maybe the large variability observed in the analytical response of distant targets. Therefore, in this work, a standoff LIBS sensor has been used to assessment of the laser beam delivering up to a distant target as well as the properties of the light emitted from the plasma induced gathered by the sensor. • Applications standoff LIBS 1. Evaluation of the Cultural Heritage: Malaga Cathedral. Cultural heritage is a valuable source of history and a unique and irreplaceable legacy of our past. While sometimes an artwork can be transported to the laboratory for its analysis, in other cases this option is not feasible. The ease compaction in mobile platforms of LIBS instrumentation for in situ analysis, allows for moving the system sensor to the location of the sample. For first time a standoff LIBS system has been used to characterize and analyze the composition of building materials as well as potential sources of contamination in a historic building on difficult to access areas, since this technology only requires a clear line of sight to the target. I. Location and identification of explosive-contaminated fingerprint. Nowadays, it is clear that the detection of explosives due to numerous terrorist attacks requires a special attention. LIBS is an attractive technology to anticipating this type of threats. In the present work, the ability of a mobile LIBS sensor to locate and identify fingerprints of explosives residues (DNT, TNT, RDX, PETN and chloratite ) on different surfaces (aluminum and glass) from a minimum distance of 30m has been demonstrated. Chemical distribution maps of the different residues with 100% effectiveness were developed. However, despite the effectiveness of the technique in the localization and detection of explosives residues, one of the main problems is the identification of products that share a similar elemental composition, and thus a similarity in the analytical response. In this memory have been developed and implemented chemometric algorithms, which are capable of adapting to different working ranges, to distinguish residues of organic explosives of traces of dairy products, such as olive oil, motor oil, hand cream, gasoline, fuel oil, etc. on a metal surface (aluminum). This strategy allows categorize the residues assessed with a 100% accuracy and error rates below 5 %. II. Forensic studies for the determination of radiological material. Although radioactivity has numerous applications in everyday life, the danger of a radiological dispersal event, either by natural causes or malicious (dirty bombs) is more than evident. Therefore, the detection and identification of explosives as well as their monitoring and quantification from a safe location is demanded. The potential of standoff LIBS to scan, analyze and quickly characterize the radiological contamination in various objects of street furniture has been here evaluated. The results have demonstrated the selectivity and sensitivity of the technology to detect radioactive surrogates such as Co, Ba, Sr, Cs, Ir and U on substrates of aluminum, clay, concrete and glass. It have been also demonstrated the capabilities of the technique for simultaneous and in situ analysis of explosive and radiological evidence in a post-detonation scenario