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Cybersecurity in Unmanned Aerial Vehicles: a Review Cover

Figures & Tables

Overview of current cybersecurity attacks in the STRIDE categories

UAV CybersecuritySTRIDE
Cybersecurity 4.1*-----
Cybersecurity 4.2.1-----*
Cybersecurity 4.2.2*-----
Cybersecurity 4.2.3*-----
Cybersecurity 4.2.4----*-
Cybersecurity 4.2.5----*-
Cybersecurity 4.3*-----
Cybersecurity 4.4----*-
Cybersecurity 4.5----*-
Cybersecurity 4.6.1*-----
Cybersecurity 4.6.2*-----
Cybersecurity 4.6.3*-----
Cybersecurity 4.6.4*-----
Cybersecurity 4.6.5---*--
Cybersecurity 4.7.1----*-
Cybersecurity 4.7.2------
Cybersecurity 4.7.3*-----
Cybersecurity 4.8-*----

Distribution of the cybersecurity attack targets

AttackSTRIDE
Eavesdropping3-----
Crashing3---4-
Landing1---4-
Media or file access---1-1
Hijacking61-11-

Cybersecurity issues and proposed solutions

IssuesMethodSolution DescriptionApplicationReference
Intrusion detection in vehicular environmentsGame theory-based approachDetecting intruders generally, and nearby in the Vehicular ad hoc network (VANET) environmentVehicular ad hoc network[22]
Cybersecurity, privacy, and public safetyEnhancement in functionalities of unmanned aerial vehicles (UAVs)Framework's overviews, followed in determining maturity at the international, regional, and national levelsUnmanned aircraft systems (UASs)/UAVs[23]
Traffic issues, and smart advancementSurveillance cameras and sensor applicationEfficient connected and autonomous vehicles need to exercise caution in the context of privacy and securityUAVs[24]
Nonsecure communicationMachine learningAn edge-aided secure lightweight guarantee technique for safe interactionInternet of drones[25]
Infection monitoring and facial recognitionPandemic situations (Covid-19)Indisputably enhances drone designAutonomous UAV deployment[26]
Cyberattacks and counterattacks at airportsUAV deploymentInstalling drone-supported facilities near areas such as airportsDrone-sensing technologies[27]
Precise landingA robust spherical-object detection algorithmInstallation of a camera on the platform controls the macro drones near the landing areaA DJI Tello drone[28]
Intrusion detectionAdvanced machine learningA two-stage hybrid approach to regulating attackInternet of things (IoT) networks[29]
Effectiveness, flexibility, and vigorBio-inspired routingHeterogeneous UAS swarm networkingUAVs[30]
UAV authenticationDeep neural networksProposed a federated learning-based UAV authentication approachUAVs[31]

Most common attack goals

AttackSTRIDE
Eavesdropping2-----
Crash/land3---4-
Media or file access---1-1
Hijacking51-11-

Goals and gears for any spoofing group attack and DoS

UAV Cybersecurity AttacksTargetsSome Gear
Any spoofing group attackDoSTampering
Attack 4.1HackingAdapter, joystick, ROS, WiFi network--
Attack 4.2.1HackingLabSat3 global positioning system (GPS) simulatorDJI software development kit (SDK)-
Attack 4.2.2HackingUnknown--
Attack 4.2.3EavesdroppingAdapter, Xbee, universal serial bus (USB) to RS232, Python interpreterDJI SDK-
Attack 4.2.4EavesdroppingXbee, Python interpreterSDK
Attack 4.2.5Eavesdropping868LP chip, Python interpreter--
Attack 4.3CrashingWiFi network adapter, virtual machine (VM)Hping3, Network, and LOIC-
Attack 4.4HackingAir-cracking--
Attack 4.5Crashing-Telnet-
Attack 4.6.1LandingAir-cracking--
Attack 4.6.2Crashing-Air-cracking-
Attack 4.6.3EavesdroppingWiFi pineapple nano--
Attack 4.6.4EavesdroppingWiFi pineapple nano--
Attack 4.6.5HackingNot specified--
Attack 4.7.1LandingNot specifiedNot specified-
Attack 4.7.2CrashingNot specifiedNot specified-
Attack 4.7.3CrashingNot specifiedNot specified-
Attack 4.8HackingMaldrone software tool-None Specified
Language: English
Submitted on: May 20, 2022
Published on: Oct 18, 2023
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2023 Wasswa Shafik, S. Mojtaba Matinkhah, Fawad Shokoor, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.