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Small Unmanned Aerial Systems (UAS) usage is undoubtedly increasing at a significant rate. However, alongside this expansion is a growing concern that dependable low-cost counter measures do not exist. To mitigate a threat in a restricted airspace, it must first be known that a threat is present. With airport disruption from malicious UASs occurrin...
Citations
... From the analysis of the flight log values for the parameters during the GPS Spoofing and GPS Jamming attacks, we can conclude that the important parameters are noise, gpsNum and FixTypeGps. For each parameter, certain numerical ranges were identified, which make it possible to determine the presence of an attack on the UAV's GPS system [13]. Based on the results of the analysis, the following conclusions can be drawn: ...
The article discusses issues related to the analysis of the UAV flight logs to identify information security incidents that occurred during flights. Existing methods and tools for analyzing logs are described, and sources for obtaining logs are presented. In the main part of the article, first, the parameters important for the analysis are highlighted. The features of analyzing the values in the flight logs for the detection of two types of attacks—GPS Spoofing and GPS Jamming are also given. For this purpose, the parameters that are most important for the detection of each of these attacks have been identified, systems of equations have been compiled to analyze these parameters, the calculations of which make it possible to detect the fact of attacks with high efficiency. The paper also presents the developed software that implements a number of functions that allow automating the analysis of flight logs, as well as determining the presence of information security incidents that occurred during the flight.
... The first category involves extracting signal features using signal processing methods such as Fourier transform, followed by classification using SVM, decision trees, and other methods [29,32]. The second category involves applying simple processing to the signal, followed by feature extraction using deep neural networks for UAV signal identification [33][34][35][36][37]. This section will provide an overview of both traditional RF-based detection methods and deep learning-based approaches for UAV detection. ...
... The accuracy of the final classification can reach about 99%. In Ref. [34], the authors fed the spectrogram directly into the CNN to extract features. With this method, the prediction accuracy can reach up to 100%. ...
... These studies shed light on the potential of leveraging edge computing for UAV detection and tracking. Carolyn J. Swinney et al. [34] introduces a cost-effective early warning system for UAV detection and classification. The system is composed of a BladeRF software-defined radio (SDR), a wideband antenna, and a Raspberry Pi 4, which together form an edge node. ...
Unmanned aerial vehicles (UAVs) have gained significant popularity across various domains, but their proliferation also raises concerns about security, public safety, and privacy. Consequently, the detection and tracking of UAVs have become crucial. Among the UAV-monitoring technologies, those suitable for urban Internet-of-Things (IoT) environments primarily include radio frequency (RF), acoustic, and visual technologies. In this article, we provide a comprehensive review of passive UAV surveillance technologies, encompassing RF-based, acoustic-based, and vision-based methods for UAV detection, localization, and tracking. Our research reveals that certain lightweight UAV depth detection models have been effectively downsized for deployment on edge devices, facilitating the integration of edge computing and deep learning. In the city-wide anti-UAV, the integration of numerous urban infrastructure monitoring facilities presents a challenge in achieving a centralized computing center due to the large volume of data. To address this, calculations can be performed on edge devices, enabling faster UAV detection. Currently, there is a wide range of anti-UAV systems that have been deployed in both commercial and military sectors to address the challenges posed by UAVs. In this article, we provide an overview of the existing military and commercial anti-UAV systems. Furthermore, we propose several suggestions for developing general-purpose UAV-monitoring systems tailored for urban environments. These suggestions encompass considering the specific requirements of the application scenario, integrating detection and tracking mechanisms with appropriate countermeasures, designing for scalability and modularity, and leveraging advanced data analytics and machine learning techniques. To promote further research in the field of UAV-monitoring systems, we have compiled publicly available datasets comprising visual, acoustic, and radio frequency data. These datasets can be employed to evaluate the effectiveness of various UAV-monitoring techniques and algorithms. All of the datasets mentioned are linked in the text or in the references. Most of these datasets have been validated in multiple studies, and researchers can find more specific information in the corresponding papers or documents. By presenting this comprehensive overview and providing valuable insights, we aim to advance the development of UAV surveillance technologies, address the challenges posed by UAV proliferation, and foster innovation in the field of UAV monitoring and security.
... Most UASs utilize RF transmissions for communication between the UAV and its associated RC [44]. This bi-directional communication involves both uplink and downlink signals, allowing for seamless information exchange. ...
The market for unmanned aerial systems (UASs) has grown considerably worldwide, but their ability to transmit sensitive information poses a threat to public safety. To counter these threats, authorities, and anti-drone organizations are ensuring that UASs comply with regulations, focusing on strategies to mitigate the risks associated with malicious drones. This study presents a technique for detecting drone models using identification (ID) tags in radio frequency (RF) signals, enabling the extraction of real-time telemetry data through the decoding of Drone ID packets. The system, implemented with a development board, facilitates efficient drone tracking. The results of a measurement campaign performance evaluation include maximum detection distances of 1.3 km for the Mavic Air, 1.5 km for the Mavic 3, and 3.7 km for the Mavic 2 Pro. The system accurately estimates a drone’s 2D position, altitude, and speed in real time. Thanks to the decoding of telemetry packets, the system demonstrates promising accuracy, with worst-case distances between estimated and actual drone positions of 35 m for the Mavic 2 Pro, 17 m for the Mavic Air, and 15 m for the Mavic 3. In addition, there is a relative error of 14% for altitude measurements and 7% for speed measurements. The reaction times calculated to secure a vulnerable site within a 200 m radius are 1.83 min (Mavic Air), 1.03 min (Mavic 3), and 2.92 min (Mavic 2 Pro). This system is proving effective in addressing emerging concerns about drone-related threats, helping to improve public safety and security.
... Implementing an early warning system for UAV intrusion based on commercially available components is an interesting and practical solution presented in [35]. The results show that a classifier could be effectively implemented on the Raspberry Pi and BladeRF-SDR, hardware that does not require extensive financial outlays. ...
Unmanned Aerial Vehicles (UAVs), sometimes known as drones, evolved from military to civilian applications, opening up novel perspectives in a variety of everyday services. The rapidly growing consumer interest in amateur drones equipped with high-end cameras compromises the everyday safety and privacy of people. In the literature, a variety of sensing techniques based on different physical phenomena have been proposed for drone detection. Among acoustic, optical, or radar detection systems, passive radiofrequency sensing is the only one that can identify a drone even before it takes off and additionally indicate the operator’s location. A spectrogram-based method is developed and optimised in terms of computing location, resulting in the possibility of sensor grid deployment over a standard Ethernet network. The detection phase involves hardware-accelerated energy sensing to extract the data frames from the background noise. Drone presence is then identified using machine learning based solely on preamble pattern recognition, which reduces the computational effort. The presented procedure is evaluated in an isolated setting employing an open-source dataset and tuned across multiple neural network architectures. Next, the complete sensor processing chain is examined in a real-life scenario. The analytical energy detector stage reaches a margin of roughly -8.7 dB in the signal-to-noise (SNR) ratio. With 1.1 M parameters, the proposed neural network achieves 99.93% simulation accuracy in up to -9.5 dB SNR range. Even after quantization for embedded platform implementation, the device can be used as a stand-alone early intrusion detector or as part of a distributed sensor grid.
