Literature DB >> 32580402

Defending Airports from UAS: A Survey on Cyber-Attacks and Counter-Drone Sensing Technologies.

Georgia Lykou1, Dimitrios Moustakas1, Dimitris Gritzalis1.   

Abstract

As the fastest growing segment of aviation, unmanned aerial systems (UAS) continue to increase in number, technical complexity and capabilities. Numerous civilian and commercial uses are drastically transforming civil protection, asset delivery, commercial and entertaining activities. However, UAS pose significant challenges in terms of safety, security and privacy within society. An increasing phenomenon, nowadays, is drone-related incidents near airport facilities, which are expected to proliferate in frequency, complexity and severity, as drones become larger and more powerful. Critical infrastructures need to be protected from such aerial attacks, through effective counteracting technologies, risk management and resilience plans. In this paper, we present a survey of drone incidents near airports and a literature review of sensor technologies, able to prevent, detect, identify and mitigate rogue drones. We exhibit the benefits and limitations of available counter-drone technologies (C-UAS); however, defending airports against misused drone activity is a hard problem. Therefore, we analyze three realistic attack scenarios from malicious drones and propose an effective C-UAS protection plan for each case. We discuss applicability limitations of C-UAS in the aviation context and propose a resilience action plan for airport stakeholders for defending against airborne threats from misused drones.

Entities:  

Keywords:  airport resilience; counter unmanned aerial systems (C-UAS); critical infrastructure protection; cyber-physical systems; drones; sensing technologies; sensors; unmanned aerial vehicles (UAV)

Year:  2020        PMID: 32580402     DOI: 10.3390/s20123537

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  6 in total

1.  Multi-Sensory Data Fusion in Terms of UAV Detection in 3D Space.

Authors:  Janusz Dudczyk; Roman Czyba; Krzysztof Skrzypczyk
Journal:  Sensors (Basel)       Date:  2022-06-07       Impact factor: 3.847

Review 2.  Threats from and Countermeasures for Unmanned Aerial and Underwater Vehicles.

Authors:  Wahab Khawaja; Vasilii Semkin; Naeem Iqbal Ratyal; Qasim Yaqoob; Jibran Gul; Ismail Guvenc
Journal:  Sensors (Basel)       Date:  2022-05-20       Impact factor: 3.847

3.  Acoustic Estimation of the Direction of Arrival of an Unmanned Aerial Vehicle Based on Frequency Tracking in the Time-Frequency Plane.

Authors:  Nathan Itare; Jean-Hugh Thomas; Kosai Raoof; Torea Blanchard
Journal:  Sensors (Basel)       Date:  2022-05-26       Impact factor: 3.847

4.  Distributed Extended Kalman Filtering Based Techniques for 3-D UAV Jamming Localization.

Authors:  Waleed Aldosari; Muhammad Moinuddin; Abdulah Jeza Aljohani; Ubaid M Al-Saggaf
Journal:  Sensors (Basel)       Date:  2020-11-10       Impact factor: 3.576

5.  Drone Model Classification Using Convolutional Neural Network Trained on Synthetic Data.

Authors:  Mariusz Wisniewski; Zeeshan A Rana; Ivan Petrunin
Journal:  J Imaging       Date:  2022-08-12

6.  Spoofing Attacks on FMCW Radars with Low-Cost Backscatter Tags.

Authors:  Antonio Lazaro; Arnau Porcel; Marc Lazaro; Ramon Villarino; David Girbau
Journal:  Sensors (Basel)       Date:  2022-03-10       Impact factor: 3.576

  6 in total

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