“…Methods like FHSS are often applied for the communication between the GS and the UAV. However, for most commercial UAVs, an AI-based classification algorithm can be imple- mented which analyzes the spectrum using Software Defined Radio (SDR) and based on features such as the hopping sequence, predicts the presence of a UAV along with details about it [95] , [96] , [97] . Also, based on the signal strength, it is possible to track and localize the UAVs.…”
Unmanned Aerial Vehicles (UAV) have revolution- ized the aircraft industry in this decade. UAVs are now capable of carrying out remote sensing, remote monitoring, courier delivery, and a lot more. A lot of research is happening on making UAVs more robust using energy harvesting techniques to have a better battery lifetime, network performance and to secure against attackers. UAV networks are many times used for unmanned missions. There have been many attacks on civilian, military, and industrial targets that were carried out using remotely controlled or automated UAVs. This continued misuse has led to research in preventing unauthorized UAVs from causing damage to life and property. In this paper, we present a literature review of UAVs, UAV attacks, and their prevention using anti-UAV techniques. We first discuss the different types of UAVs, the regulatory laws for UAV activities, their use cases, recreational, and military UAV incidents. After understanding their operation, various techniques for monitoring and preventing UAV attacks are described along with case studies.
“…Methods like FHSS are often applied for the communication between the GS and the UAV. However, for most commercial UAVs, an AI-based classification algorithm can be imple- mented which analyzes the spectrum using Software Defined Radio (SDR) and based on features such as the hopping sequence, predicts the presence of a UAV along with details about it [95] , [96] , [97] . Also, based on the signal strength, it is possible to track and localize the UAVs.…”
Unmanned Aerial Vehicles (UAV) have revolution- ized the aircraft industry in this decade. UAVs are now capable of carrying out remote sensing, remote monitoring, courier delivery, and a lot more. A lot of research is happening on making UAVs more robust using energy harvesting techniques to have a better battery lifetime, network performance and to secure against attackers. UAV networks are many times used for unmanned missions. There have been many attacks on civilian, military, and industrial targets that were carried out using remotely controlled or automated UAVs. This continued misuse has led to research in preventing unauthorized UAVs from causing damage to life and property. In this paper, we present a literature review of UAVs, UAV attacks, and their prevention using anti-UAV techniques. We first discuss the different types of UAVs, the regulatory laws for UAV activities, their use cases, recreational, and military UAV incidents. After understanding their operation, various techniques for monitoring and preventing UAV attacks are described along with case studies.
“…In [11], drones are detected by analyzing the RF background activities along with the RF signals emitted when the drones are operated in different modes. Afterward, RF spectrum of the drone signal is computed using the discrete Fourier transform (DFT).…”
Section: A Rf Fingerprinting-based Uav Detection and Classificationmentioning
This paper investigates the problem of detection and classification of unmanned aerial vehicles (UAVs) in the presence of wireless interference signals using a passive radio frequency (RF) surveillance system. The system uses a multistage detector to distinguish signals transmitted by a UAV controller from the background noise and interference signals. First, RF signals from any source are detected using a Markov models-based naïve Bayes decision mechanism. When the receiver operates at a signal-to-noise ratio (SNR) of 10 dB, and the threshold, which defines the states of the models, is set at a level 3.5 times the standard deviation of the preprocessed noise data, a detection accuracy of 99.8% with a false alarm rate of 2.8% is achieved. Second, signals from Wi-Fi and Bluetooth emitters, if present, are detected based on the bandwidth and modulation features of the detected RF signal. Once the input signal is identified as a UAV controller signal, it is classified using machine learning (ML) techniques. Fifteen statistical features extracted from the energy transients of the UAV controller signals are fed to neighborhood component analysis (NCA), and the three most significant features are selected. The performance of the NCA and five different ML classifiers are studied for 15 different types of UAV controllers. A classification accuracy of 98.13% is achieved by k-nearest neighbor classifier at 25 dB SNR. Classification performance is also investigated at different SNR levels and for a set of 17 UAV controllers which includes two pairs from the same UAV controller models.
“…2NI USRP-2943R RF receiver [4].Fig. 3a: Front panel of the LabVIEW program installed on the laptops to capture the drones' RF communication [1]. b: Block diagram of LabVIEW program [1].Fig.…”
Section: Datamentioning
confidence: 99%
“…3a: Front panel of the LabVIEW program installed on the laptops to capture the drones' RF communication [1]. b: Block diagram of LabVIEW program [1].Fig. 4RF activities plots with normalized amplitudes between −1 and 1.…”
Section: Datamentioning
confidence: 99%
“…The horizontal dashed red lines define the levels. BUI is a Binary Unique Identifier for each component to be used in labelling [1].…”
Modern technology has pushed us into the information age, making it easier to generate and record vast quantities of new data. Datasets can help in analyzing the situation to give a better understanding, and more importantly, decision making. Consequently, datasets, and uses to which they can be put, have become increasingly valuable commodities. This article describes the DroneRF dataset: a radio frequency (RF) based dataset of drones functioning in different modes, including off, on and connected, hovering, flying, and video recording. The dataset contains recordings of RF activities, composed of 227 recorded segments collected from 3 different drones, as well as recordings of background RF activities with no drones. The data has been collected by RF receivers that intercepts the drone's communications with the flight control module. The receivers are connected to two laptops, via PCIe cables, that runs a program responsible for fetching, processing and storing the sensed RF data in a database. An example of how this dataset can be interpreted and used can be found in the related research article “RF-based drone detection and identification using deep learning approaches: an initiative towards a large open source drone database” (Al-Sa'd et al., 2019).
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