Signal fingerprinting and machine learning framework for UAV detection and identification.

Advancement in technology has led to creative and innovative inventions. One such invention includes unmanned aerial vehicles (UAVs). UAVs (also known as drones) are now an intrinsic part of our society because their application is becoming ubiquitous in every industry ranging from transportation and logistics to environmental monitoring among others. With the numerous benign applications of UAVs, their emergence has added a new dimension to privacy and security issues. There are little or no strict regulations on the people that can purchase or own a UAV. For this reason, nefarious actors can take advantage of these aircraft to intrude into restricted or private areas. A UAV detection and identification system is one of the ways of detecting and identifying the presence of a UAV in an area. UAV detection and identification systems employ different sensing techniques such as radio frequency (RF) signals, video, sounds, and thermal imaging for detecting an intruding UAV. Because of the passive nature (stealth) of RF sensing techniques, the ability to exploit RF sensing for identification of UAV flight mode (i.e., flying, hovering, videoing, etc.), and the capability to detect a UAV at beyond visual line-of-sight (BVLOS) or marginal line-of-sight makes RF sensing techniques promising for UAV detection and identification. More so, there is constant communication between a UAV and its ground station (i.e., flight controller). The RF signals emitting from a UAV or UAV flight controller can be exploited for UAV detection and identification. Hence, in this work, an RF-based UAV detection and identification system is proposed and investigated. In RF signal fingerprinting research, the transient and steady state of the RF signals can be used to extract a unique signature. The first part of this work is to use two different wavelet analytic transforms (i.e., continuous wavelet transform and wavelet scattering transform) to investigate and analyze the characteristics or impacts of using either state for UAV detection and identification. Coefficient-based and image-based signatures are proposed for each of the wavelet analysis transforms to detect and identify a UAV. One of the challenges of using RF sensing is that a UAV\u27s communication links operate at the industrial, scientific, and medical (ISM) band. Several devices such as Bluetooth and WiFi operate at the ISM band as well, so discriminating UAVs from other ISM devices is not a trivial task. A semi-supervised anomaly detection approach is explored and proposed in this research to differentiate UAVs from Bluetooth and WiFi devices. Both time-frequency analytical approaches and unsupervised deep neural network techniques (i.e., denoising autoencoder) are used differently for feature extraction. Finally, a hierarchical classification framework for UAV identification is proposed for the identification of the type of unmanned aerial system signal (UAV or UAV controller signal), the UAV model, and the operational mode of the UAV. This is a shift from a flat classification approach. The hierarchical learning approach provides a level-by-level classification that can be useful for identifying an intruding UAV. The proposed frameworks described here can be extended to the detection of rogue RF devices in an environment

Audio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks

Drones are becoming increasingly popular not only for recreational purposes but in day-to-day applications in engineering, medicine, logistics, security and others. In addition to their useful applications, an alarming concern in regard to the physical infrastructure security, safety and privacy has arisen due to the potential of their use in malicious activities. To address this problem, we propose a novel solution that automates the drone detection and identification processes using a drone’s acoustic features with different deep learning algorithms. However, the lack of acoustic drone datasets hinders the ability to implement an effective solution. In this paper, we aim to fill this gap by introducing a hybrid drone acoustic dataset composed of recorded drone audio clips and artificially generated drone audio samples using a state-of-the-art deep learning technique known as the Generative Adversarial Network. Furthermore, we examine the effectiveness of using drone audio with different deep learning algorithms, namely, the Convolutional Neural Network, the Recurrent Neural Network and the Convolutional Recurrent Neural Network in drone detection and identification. Moreover, we investigate the impact of our proposed hybrid dataset in drone detection. Our findings prove the advantage of using deep learning techniques for drone detection and identification while confirming our hypothesis on the benefits of using the Generative Adversarial Networks to generate real-like drone audio clips with an aim of enhancing the detection of new and unfamiliar drones

Digital Interaction and Machine Intelligence

This book is open access, which means that you have free and unlimited access. This book presents the Proceedings of the 9th Machine Intelligence and Digital Interaction Conference. Significant progress in the development of artificial intelligence (AI) and its wider use in many interactive products are quickly transforming further areas of our life, which results in the emergence of various new social phenomena. Many countries have been making efforts to understand these phenomena and find answers on how to put the development of artificial intelligence on the right track to support the common good of people and societies. These attempts require interdisciplinary actions, covering not only science disciplines involved in the development of artificial intelligence and human-computer interaction but also close cooperation between researchers and practitioners. For this reason, the main goal of the MIDI conference held on 9-10.12.2021 as a virtual event is to integrate two, until recently, independent fields of research in computer science: broadly understood artificial intelligence and human-technology interaction

Marine biodiversity assessments using aquatic internet of things

While Ubiquitous Computing remains vastly applied in urban environments, it is still scarce in oceanic environments. Current equipment used for biodiversity assessments remain at a high cost, being still inaccessible to wider audiences. More accessible IoT (Internet of Things) solutions need to be implemented to tackle these issues and provide alternatives to facilitate data collection in-the-wild. While the ocean remains a very harsh environment to apply such devices, it is still providing the opportunity to further explore the biodiversity, being that current marine taxa is estimated to be 200k, while this number can be actually in millions. The main goal of this thesis is to provide an apparatus and architecture for aerial marine biodiversity assessments, based on low-cost MCUs (Microcontroller unit) and microcomputers. In addition, the apparatus will provide a proof of concept for collecting and classifying the collected media. The thesis will also explore and contribute to the latest IoT and machine learning techniques (e.g. Python, TensorFlow) when applied to ocean settings. The final product of the thesis will enhance the state of the art in technologies applied to marine biology assessments.A computação ubíqua é imensamente utilizada em ambientes urbanos, mas ainda é escassa em ambientes oceânicos. Os equipamentos atuais utilizados para o estudo de biodiversidade são de custo alto, e geralmente inacessíveis para o público geral. Uma solução IoT mais acessível necessita de ser implementada para combater estes problemas e fornecer alternativas para facilitar a recolha de dados na natureza. Embora o oceano seja um ambiente severo para aplicar estes dispositivos, este fornece mais oportunidades para explorar a biodiversidade, sendo que a taxa de marinha atual é estimada ser 200 mil, mas este número pode estar nos milhões. O objetivo principal desta tese é fornecer um aparelho e uma arquitetura para estudos aéreos de biodiversidade marinha, baseado em microcontroladores low-cost e microcomputadores. Adi cionalmente, este aparelho irá fornecer uma prova de conceito para coletar e classificar a mídia coletada. A tese irá também explorar e contribuir para as técnicas mais recentes de machine learn ing (e.g. Python, TensorFlow) quando aplicadas num cenário de oceano. O produto final desta tese vai elevar o estado da arte em tecnologias aplicadas a estudos de biologia marinha