Detecting drones using machine learning

Drones are becoming an increasing part of the ever-connected society that we currently live in. Drones are used for delivering packages, geographic surveying, assessing the health of crops or just good old fashioned fun. Drones are excellent tools and their uses are expected to expand in the future. Yet, drones can be easily misused for malicious purposes if drone security isn\u27t taken more seriously. One of the bigger problems drones have been causing lately is that they are being used to capture images or video of disasters, such as wildfires and in doing so get in the way of the relief effort. They also have caused several problems by flying too close to airports. These drones are usually too small for radar to pick up and are often discovered by visual means and by that time it is too late. One defense against this has been GPS designated no fly zones, however, this can be easily overcome by spoofing the GPS signal to make the drone think it is in a safe area to fly. In this paper, I examine ways of detecting the presence of a drone using machine learning models by recording the RF spectrum during a drone’s flight and then feeding the raw data into a machine learning model. This could be used around airports or even on the airplanes themselves to detect the presence and/or approach of a drone. Specifically, I examine two very popular consumer drones and their transmitters: The 3D Robotics Solo and the DJI Phantom 2. These two types of drones are unique in the way that they send and receive signals to the transmitter. I show that machine learning models, once trained, can detect drone activity in the RF spectrum. However, more work is needed in order to improve the detection rate of these models so that they may be employed in a practical manner

A SYSTEMS ANALYSIS OF ENERGY USAGE AND EFFECTIVENESS OF A COUNTER-UNMANNED AERIAL SYSTEM USING A CYBER-ATTACK APPROACH

Existing counter-unmanned aerial systems (C-UAS) rely heavily on radio frequency (RF) jamming techniques that require a large amount of energy. RF jamming results in undesirable consequences such as jamming nearby friendly devices as well as increasing RF footprint of local operators. Current cybersecurity analysis of commercial-off-the shelf (COTS) UASs have revealed vulnerabilities that can be used to conduct C-UAS operations in the cyber domain via cyber-attacks that hijack device-specific communication links on narrow RF bands. This thesis validates the cyber-attack C-UAS (CyC-UAS) concept through reviewing recent C-UAS operational experimental scenarios and conducting analysis on the collected data. Then, a model of a defense facility is constructed to analyze and validate specific mission scenarios and several proposed concepts of operation. A comparison of the energy requirements between CyC-UAS and existing C-UAS techniques is performed to assess energy efficiency and trade-offs of different C-UAS approaches. The comparison of energy requirements between the CyC-UAS prototype and existing C-UAS RF jamming products shows CyC-UAS has significant energy savings while not affecting other telecommunication devices operating at the same frequencies. CyC-UAS is able to achieve the same mission by consuming much less energy and shows promise as a new, lower energy, and lower collateral damage approach to defending against UASs.Outstanding ThesisMajor, Republic of Singapore Air ForceApproved for public release. Distribution is unlimited

An Integrated Framework for Sensing Radio Frequency Spectrum Attacks on Medical Delivery Drones

Drone susceptibility to jamming or spoofing attacks of GPS, RF, Wi-Fi, and operator signals presents a danger to future medical delivery systems. A detection framework capable of sensing attacks on drones could provide the capability for active responses. The identification of interference attacks has applicability in medical delivery, disaster zone relief, and FAA enforcement against illegal jamming activities. A gap exists in the literature for solo or swarm-based drones to identify radio frequency spectrum attacks. Any non-delivery specific function, such as attack sensing, added to a drone involves a weight increase and additional complexity; therefore, the value must exceed the disadvantages. Medical delivery, high-value cargo, and disaster zone applications could present a value proposition which overcomes the additional costs. The paper examines types of attacks against drones and describes a framework for designing an attack detection system with active response capabilities for improving the reliability of delivery and other medical applications.Comment: 7 pages, 1 figures, 5 table

Radio Frequency Toolbox for Drone Detection and Classification

The continuous development of inexpensive embedded sensors has led to the rapid proliferation of new civilian use of unmanned aerial vehicle (UAVs) or drones. It is now easier for civilians to own drones as the cost falls. As we all know drones have a variety of important applications and can also be used for negative effects too. These drones can pose a threat to the security of the population either civilian, organization or industry. There is a need for Radio Frequency Signal Classification (RF-Class) toolbox which can monitor, detect, and classify RF signals from drone communication system. The ability to accurately classify over-the-air radio signals will provide insights into spectrum utilization, device fingerprinting and protocol identification. These insights can help the Warfighter to constantly be informed about adversaries transmitters capabilities without their knowledge. The advantage of the drone detection and classification toolbox is extracting information about transmitters and providing receivers information about transmitted signals. The classification of RF signals will be done based on the modulation scheme, in this case, orthogonal frequency division multiplexing (OFDM). The signal energy and features from the signals leveraging its orthogonal frequency division multiplexing (OFDM) parameter information will be used to classify the signal. This classification will be done using the capabilities of machine learning to train and test the information collected. The content of this thesis discusses how drone detection and classification can be achieved using software defined radio. GNU radio and other hardware components will be used to implement a simulation of the module

A Comprehensive Review of Unmanned Aerial Vehicle Attacks and Neutralization Techniques

Unmanned Aerial Vehicles (UAV) have revolutionized 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

Effectiveness of Electronic Counter Measures (ECM) on Small Unmanned Aerial Systems (SUAS): Analysis and Preliminary Tests

Small unmanned aerial systems (sUAS) have become more common and affordable for government, commercial, and private use. There are several counter sUAS products that employ electromagnetic counter measures to disrupt the communications link of sUAS. However, most of these solutions are limited in efficacy to specific sUAS types due to the sophisticated control and communications link technologies utilized by sUAS which make it challenging to effectively jam. To address these challenges, a Drone Detection and Mitigation Radar (DDMR) concept was developed. The jamming component of the DDMR used wideband noise combined with random sweeping of the noise to jam the communications link. This thesis research was predicated by a laboratory experiment which used the DDMR system to successfully jam an sUAS’s communications link. This particular experiment did not (1) provide any theoretical analysis, (2) simulation analysis to determine the effective jamming probabilities, or (3) conduct additional experiments to find the optimal sweeping frequency for the jamming component of the DDMR. This thesis focuses on the optimization of the sweeping noise jamming solution of the communications link by examining the theoretical and simulation analysis as well as the results of further experimental studies. The findings are presented in this thesis paper

REDESIGNING THE COUNTER UNMANNED SYSTEMS ARCHITECTURE

Includes supplementary material. Please contact [email protected] for access.When the Islamic State used Unmanned Aerial Vehicles (UAV) to target coalition forces in 2014, the use of UAVs rapidly expanded, giving weak states and non-state actors an asymmetric advantage over their technologically superior foes. This asymmetry led the Department of Defense (DOD) and the Department of Homeland Security (DHS) to spend vast sums of money on counter-unmanned aircraft systems (C-UAS). Despite the market density, many C-UAS technologies use expensive, bulky, and high-power-consuming electronic attack methods for ground-to-air interdiction. This thesis outlines the current technology used for C-UAS and proposes a defense-in-depth framework using airborne C-UAS patrols outfitted with cyber-attack capabilities. Using aerial interdiction, this thesis develops a novel C-UAS device called the Detachable Drone Hijacker—a low-size, weight, and power C-UAS device designed to deliver cyber-attacks against commercial UAVs using the IEEE 802.11 wireless communication specification. The experimentation results show that the Detachable Drone Hijacker, which weighs 400 grams, consumes one Watt of power, and costs $250, can interdict adversarial UAVs with no unintended collateral damage. This thesis recommends that the DOD and DHS incorporates aerial interdiction to support its C-UAS defense-in-depth, using technologies similar to the Detachable Drone Hijacker.DASN-OE, Washington DC, 20310Captain, United States Marine CorpsApproved for public release. Distribution is unlimited

Development of improvements in UAS for difficult access environments

The objective of this document is to study and verify the development and improvements in Unmanned Aircraft Systems (UAS) for difficult access environments since this matter is a critical area of research and innovation. As the use of UAS in various applications continues to expand, the need for these systems to operate in challenging environments such as mountainous terrain, dense forests, or urban areas with high-rise structures is increasing. The main motivation to start developing this project was the challenge exposed in the Xprize Rainforest Competition. The $10M XPRIZE Rainforest is a five-year competition to enhance the understanding of the rainforest ecosystem. I am part of the semifinalist team, Providence Plus, a multidisciplinary team composed by scientists from UPC, CSIC, MIT, and TUDelf. The purpose of this challenge is to obtain the maximum amount of information on biodiversity in the rainforest, using drone technology in this type of environment, with all the difficulties inherent in this environment that must be overcome and that are also the subject of analysis in this work, to propose and compare the different solutions and technologies to achieve the objectives of said challenge. As resources for competing in Xprize Challenge are limited and the final solution shall be scalable, the technologies evaluated must be cost efficient and practical. The first difficulty in this kind of environments is the signal strength and signal quality, not only for the drone commands but for the video and telemetry data. In this work, different solutions will be compared since analogic to digital technology. The second difficulty is autonomy, in terms of energetic supply. Taking into account the Rainforest environment and environmental policies, the most suitable technology available is batteries. There are several types of batteries that are suitable for drones, depending on the size, weight, and specifications of the drone. There will be a comparison between the most popular ones. Apart from that, an analysis of different propulsion configurations (ideal motors and propellers) will be carried out in order to achieve an optimal flight time without compromising the structural integrity of the drone. The third difficulty is reducing noise levels, in order to avoid disturbing the wildlife and with the goal in mind of having the best images possible, a study of different propellers will be carried out. Finally, durability and weather resistance: Rainforests are characterized by high humidity, heavy rainfall, and extreme heat. Drones used in this environment must be built to withstand these conditions and be weather-resistant. This may involve using materials that can withstand moisture, designing waterproof housing for sensitive components, and installing heat dissipation systems to prevent overheating.Objectius de Desenvolupament Sostenible::15 - Vida d'Ecosistemes TerrestresObjectius de Desenvolupament Sostenible::13 - Acció per al Clim