The DOI matches its Crossref record; 79 of the 79 references it cites were found in a registry or an index.
Defending Airports from UAS: A Survey on Cyber-Attacks and Counter-Drone Sensing Technologies
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Figures
![Figure 1 Potential civilian and commercial uses for small UAS [11].](/paper-figures/89d760ab4c75440f9732164598774037/defending-airports-from-uas-a-survey-on-cyber-attacks-and-counter-drone-sensing-figure-1.jpg)

![Figure 3 Detailed three-dimensional geofencing solutions around airports [39].](/paper-figures/48927e614eb544949953a37222f776b1/defending-airports-from-uas-a-survey-on-cyber-attacks-and-counter-drone-sensing-figure-3.jpg)










Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“To prevent those misuses as in [150-154], counter-drone technology is widely under study, which for example, enables the configuration of guarded areas to either permit or block drones.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [151]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Knott, E. F., Shaeffer, J. F., & Tuley, M. T. (2004) · Institution of Engineering and Technology eBooks · doi:10.1049/sbra026e · Cited by 1,824
A Dataset and Taxonomy for Urban Sound Research
Salamon, J., Jacoby, C., & Bello, J. P. (2014) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2647868.2655045 · Cited by 1,355
Unmanned Aircraft Capture and Control Via GPS Spoofing
Kerns, A. J., Shepard, D. P., Bhatti, J., & Humphreys, T. (2014) · J. Field Robotics · doi:10.1002/rob.21513 · Cited by 709
On the requirements for successful GPS spoofing attacks
Tippenhauer, N. O., Pöpper, C., Rasmussen, K. B., & Čapkun, S. (2011) · Conference on Computer and Communications Security · doi:10.1145/2046707.2046719 · Cited by 625
Survey on Collaborative Smart Drones and Internet of Things for Improving Smartness of Smart Cities
Alsamhi, S. H., Ma, O., Ansari, M. S., & Almalki, F. A. (2019) · IEEE Access · doi:10.1109/access.2019.2934998 · Cited by 441
Security, Privacy, and Safety Aspects of Civilian Drones: A Survey
Altawy, R., & Youssef, A. M. (2017) · ACM Transactions on Cyber-Physical Systems · doi:10.1145/3001836 · Cited by 428
The vSLAM Algorithm for Robust Localization and Mapping
Karlsson, N., Bernardo, E., Ostrowski, J., Goncalves, L., Pirjanian, P., & Munich, M. E. (2006) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2005.1570091 · Cited by 309
Classification of small UAVs and birds by micro-Doppler signatures
Molchanov, P., Harmanny, R. I. A., de Wit, J. J. M., Egiazarian, K., & Astola, J. T. (2014) · International Journal of Microwave and Wireless Technologies · doi:10.1017/s1759078714000282 · Cited by 296
Using deep networks for drone detection
Aker, C., & Kalkan, S. (2017) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2017.8078539 · Cited by 226
Deep Learning on Multi Sensor Data for Counter UAV Applications—A Systematic Review
Samaras, S., Diamantidou, E., Ataloglou, D., Sakellariou, N., Vafeiadis, A., Magoulianitis, V., Lalas, A. X., Dimou, A., Zarpalas, D., Votis, K., et al. (2019) · Sensors · doi:10.3390/s19224837 · Cited by 226
Ghost in the Air(Traffic): On insecurity of ADS-B protocol and practical attacks on ADS-B devices
Costin, A. (2012) · Graduate School and Research Center in Digital Science (EURECOM) · Cited by 211
The State-of-the-Art of Human–Drone Interaction: A Survey
Tezza, D., & Andujar, M. (2019) · IEEE Access · doi:10.1109/access.2019.2953900 · Cited by 211
Investigating Cost-effective RF-based Detection of Drones
Nguyen, P., Ravindranatha, M., Nguyen, A. D., Han, R. Y., & Vu, T. N. (2016) · ACM Workshop on Micro Aerial Vehicle Networks, Systems, and Applications (DroNet) · doi:10.1145/2935620.2935632 · Cited by 197
Micro-Doppler based detection and tracking of UAVs with multistatic radar
Hoffmann, F., Ritchie, M., Fioranelli, F., Charlish, A., & Griffiths, H. (2016) · IEEE Radar Conference (RadarConf) · doi:10.1109/radar.2016.7485236 · Cited by 194
A study on detecting drones using deep convolutional neural networks
Saqib, M., Khan, S. D., Sharma, N., & Blumenstein, M. (2017) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2017.8078541 · Cited by 185
Drone detection by acoustic signature identification
Bernardini, A., Mangiatordi, F., Pallotti, E., & Capodiferro, L. (2017) · Electronic Imaging · doi:10.2352/issn.2470-1173.2017.10.imawm-168 · Cited by 180
Exploring Civil Drone Accidents and Incidents to Help Prevent Potential Air Disasters
Wild, G., Murray, J., & Baxter, G. (2016) · Aerospace · doi:10.3390/aerospace3030022 · Cited by 168
Ritchie, M., Fioranelli, F., Griffiths, H., & Torvik, B. (2015) · IEEE Radar Conference (RadarConf) · doi:10.1109/radarconf.2015.7411926 · Cited by 156
Empirical study of drone sound detection in real-life environment with deep neural networks
Jeon, S., Shin, J.-W., Lee, Y., Kim, W.-H., Kwon, Y., & Yang, H.-Y. (2017) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco.2017.8081531 · Cited by 154
Manesh, M. R., & Kaabouch, N. (2017) · International Journal of Critical Infrastructure Protection · doi:10.1016/j.ijcip.2017.10.002 · Cited by 153
Classification of loaded/unloaded micro‐drones using multistatic radar
Fioranelli, F., Ritchie, M., Griffiths, H., & Borrion, H. (2015) · Electronics Letters · doi:10.1049/el.2015.3038 · Cited by 146
Radar micro-Doppler feature extraction using the spectrogram and the cepstrogram
Harmanny, R. I. A., de Wit, J. J. M., & Cabic, G. P. (2014) · European Radar Conference (EuRAD) · doi:10.1109/eurad.2014.6991233 · Cited by 139
Vision-Based Detection and Distance Estimation of Micro Unmanned Aerial Vehicles
Gökçe, F., Üçoluk, G., Şahi̇n, E., & Kalkan, S. (2015) · Sensors · doi:10.3390/s150923805 · Cited by 135
35 GHz FMCW drone detection system
Drozdowicz, J., Wielgo, M., Samczyński, P., Kulpa, K., Krzonkalla, J., Mordzonek, M., Bryl, M., & Jakielaszek, Z. (2016) · International Radar Symposium · doi:10.1109/irs.2016.7497351 · Cited by 135
Nguyen, P., Truong, H., Ravindranathan, M., Nguyen, A. D., Han, R. Y., & Vu, T. N. (2017) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/3081333.3081354 · Cited by 134
Real-time UAV sound detection and analysis system
Kim, J., Park, C., Ahn, J., Ko, Y., Park, J., & Gallagher, J. C. (2017) · IEEE Sensors Applications Symposium (SAS) · doi:10.1109/sas.2017.7894058 · Cited by 128
Flying objects detection from a single moving camera
Rozantsev, A., Lepetit, V., & Fua, P. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299040 · Cited by 125
Controlling UAVs with sensor input spoofing attacks
Davidson, D., Wu, H., Jellinek, R., Ristenpart, T., & Singh, V. (2016) · Cited by 122
LED-it-GO: Leaking (A Lot of) Data from Air-Gapped Computers via the (Small) Hard Drive LED
Guri, M., Zadov, B., & Elovici, Y. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-60876-1_8 · Cited by 115
A Surveillance System for Drone Localization and Tracking Using Acoustic Arrays
Chang, X., Yang, C., Wu, J., Shi, X., & Shi, Z. (2018) · IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) · doi:10.1109/sam.2018.8448409 · Cited by 107
Smart Airport Cybersecurity: Threat Mitigation and Cyber Resilience Controls
Lykou, G., Anagnostopoulou, A., & Gritzalis, D. A. (2018) · Sensors · doi:10.3390/s19010019 · Cited by 100
Self-citationA Vision-Based Approach to UAV Detection and Tracking in Cooperative Applications
Opromolla, R., Fasano, G., & Accardo, D. (2018) · Italian National Conference on Sensors · doi:10.3390/s18103391 · Cited by 99
A trajectory tracking and 3D positioning controller for the AR.Drone quadrotor
Santana, L. V., Brandao, A. S., Sarcinelli-Filho, M., & Carelli, R. (2014) · International Conference on Unmanned Aircraft Systems (ICUAS) · doi:10.1109/icuas.2014.6842321 · Cited by 98
RFID Technology-based Exploration and SLAM for Search And Rescue
Kleiner, A., Prediger, J., & Nebel, B. (2006) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2006.281867 · Cited by 98
Flying Objects Detection from a Single Moving CameraPreprint
Rozantsev, A., Lepetit, V., & Fua, P. (2016) · Cited by 85
Classification of drones based on micro-Doppler signatures with dual-band radar sensors
Zhang, P., Yang, L., Gao, C., & Li, G. (2017) · Photonics & Electromagnetics Research Symposium - Fall (PIERS-Fall) · doi:10.1109/piers-fall.2017.8293214 · Cited by 80
3D through-wall imaging with unmanned aerial vehicles using wifi
Karanam, C. R., & Mostofi, Y. (2017) · ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) · doi:10.1145/3055031.3055084 · Cited by 74
Robust drone detection for day/night counter-UAV with static VIS and SWIR cameras
Müller, T. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2262575 · Cited by 74
Micro-Doppler analysis of small UAVs
de Wit, J. J. M., Harmanny, R. I. A., & Premel-Cabic, G. (2012) · Cited by 68
Drone sound detection by correlation
Mezei, J., & Molnár, A. (2016) · IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI) · doi:10.1109/saci.2016.7507430 · Cited by 65
Main types of attacks in wireless sensor networks
Lupu, T.-G. (2009) · Cited by 64
Using Shape Descriptors for UAV Detection
Unlu, E., Zenou, E., & Riviere, N. (2018) · Electronic Imaging · doi:10.2352/issn.2470-1173.2018.09.srv-128 · Cited by 59
A Friendly and Low-Cost Technique for Capturing Non-Cooperative Civilian Unmanned Aerial Vehicles
He, D., Qiao, Y., Chen, S., Du, X., Chen, W., Zhu, S., & Guizani, M. (2018) · IEEE Network · doi:10.1109/mnet.2018.1800065 · Cited by 59
Detection of unmanned aerial vehicles using a visible camera system
Hu, S., Goldman, G. H., & Borel-Donohue, C. C. (2017) · Applied Optics · doi:10.1364/ao.56.00b214 · Cited by 53
Nassi, B., Shabtai, A., Masuoka, R., & Elovici, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.05155 · Cited by 53
Efficient drone hijacking detection using onboard motion sensors
Feng, Z., Guan, N., Lv, M., Liu, W., Deng, Q., Liu, X., & Yi, W. (2017) · Design, Automation, and Test in Europe · doi:10.23919/date.2017.7927214 · Cited by 51
Wi-Fly?: Detecting Privacy Invasion Attacks by Consumer Drones
Birnbach, S., Baker, R., & Martinović, I. (2017) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2017.23335 · Cited by 49
de Haag, M. U., Bartone, C. G., & Braasch, M. S. (2016) · IEEE/AIAA Digital Avionics Systems Conference (DASC) · doi:10.1109/dasc.2016.7778108 · Cited by 47
Low-Altitude UAS Traffic Coordination with Dynamic Geofencing
Zhu, G., & Wei, P. (2016) · AIAA Aviation Technology, Integration, and Operations Conference (ATIO) · doi:10.2514/6.2016-3453 · Cited by 44
Cross-spectral visual simultaneous localization and mapping (SLAM) with sensor handover
Magnabosco, M., & Breckon, T. P. (2012) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2012.09.023 · Cited by 41
Cost-Effective and Passive RF-Based Drone Presence Detection and Characterization
Nguyen, P., Truong, H., Ravindranathan, M., Nguyen, A. D., Han, R. Y., & Vu, T. N. (2018) · GetMobile Mobile Computing and Communications · doi:10.1145/3191789.3191800 · Cited by 41
Towards detection and control of civilian unmanned aerial vehicles
Peacock, M., & Johnstone, M. N. (2013) · Australasian Journal of Paramedicine · doi:10.4225/75/57a847dfbefb5 · Cited by 40
Park, S., Shin, S., Kim, Y., Matson, E. T., Lee, K., Slater, J. C., Scherreik, M. D., Sam, M., Gallagher, J. C., Kolodzy, P. J., et al. (2015) · Proceedings of IEEE Sensors/Proceedings of IEEE Sensors ... · doi:10.1109/icsens.2015.7370533 · Cited by 40
Flight Dynamics-Based Recovery of a UAV Trajectory Using Ground Cameras
Rozantsev, A., Sinha, S. N., Dey, D., & Fua, P. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.266 · Cited by 38
Passive acoustic system for tracking low‐flying aircraft
Sedunov, A., Sutin, A. M., Sedunov, N., Salloum, H., Yakubovskiy, A. M., & Masters, D. (2016) · IET Radar Sonar & Navigation · doi:10.1049/iet-rsn.2016.0159 · Cited by 37
An Uncrewed Aerial Vehicle Attack Scenario and Trustworthy Repair Architecture
Highnam, K., Angstadt, K., Leach, K., Weimer, W. R., Paulos, A., & Hurley, P. (2016) · International Conference on Dependable Systems and Networks workshops · doi:10.1109/dsn-w.2016.63 · Cited by 33
Aviation Cybersecurity and Cyber-Resilience: Assessing Risk in Air Traffic Management
Lykou, G., Iakovakis, G., & Gritzalis, D. A. (2019) · Advanced sciences and technologies for security applications · doi:10.1007/978-3-030-00024-0_13 · Cited by 33
Self-citationPlatform-Independent Geofencing for Low Altitude UAS Operations
Stevens, M. N., Coloe, B. T., & Atkins, E. M. (2015) · AIAA Aviation Technology, Integration, and Operations Conference (ATIO) · doi:10.2514/6.2015-3329 · Cited by 28
Detection of LSSUAV using hash fingerprint based SVDD
Shi, Z., Huang, M., Zhao, C., Huang, L., Du, X., & Zhao, Y. (2017) · International Conference on Communications · doi:10.1109/icc.2017.7996844 · Cited by 28
Hyperspectral Imaging for Real-Time Unmanned Aerial Vehicle Maritime Target Detection
Freitas, S., Silva, H. M., Almeida, J. M., & Silva, E. (2017) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-017-0689-0 · Cited by 28
Multi-Mode Guidance for an Independent Multicopter Geofencing System
Stevens, M. N., & Atkins, E. M. (2016) · AIAA Aviation Technology, Integration, and Operations Conference (ATIO) · doi:10.2514/6.2016-3150 · Cited by 27
Implementing Cyber-Security Measures in Airports to Improve Cyber-Resilience
Lykou, G., Anagnostopoulou, A., & Gritzalis, D. A. (2018) · Global Internet of Things Summit (GIoTS) · doi:10.1109/giots.2018.8534523 · Cited by 24
Self-citationA case study for assured containment
Hayhurst, K. J., Maddalon, J. M., Neogi, N. A., & Verstynen, H. A. (2015) · International Conference on Unmanned Aircraft Systems (ICUAS) · doi:10.1109/icuas.2015.7152299 · Cited by 23
Counter Unmanned Aerial Systems Testing: Evaluation of VIS SWIR MWIR and LWIR passive imagers.
Birch, G. C., & Woo, B. L. (2017) · doi:10.2172/1342469 · Cited by 23
Geofencing in Immediate Reaches Airspace for Unmanned Aircraft System Traffic Management
Stevens, M. N., & Atkins, E. M. (2018) · AIAA Information Systems-AIAA Infotech @ Aerospace · doi:10.2514/6.2018-2140 · Cited by 23
Calantropio, A. (2019) · Safety · doi:10.3390/safety5040064 · Cited by 21
Airborne Object Detection Using Hyperspectral Imaging: Deep Learning Review
Pham, T. T. T., Takalkar, M. A., Xu, M. G., Hoang, D. T., Truong, H.-N., Dutkiewicz, E., & Perry, S. W. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-24289-3_23 · Cited by 19
UAV Localization Using Panoramic Thermal Cameras
Thomas, A., Leboucher, V., Cotinat, A., Finet, P., & Gilbert, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34995-0_69 · Cited by 19
Generic Fourier Descriptors for Autonomous UAV Detection
Unlu, E., Zenou, E., & Riviere, N. (2018) · International Conference on Pattern Recognition Applications and Methods · doi:10.5220/0006680105500554 · Cited by 18
Aerial and surface security applications using lidar
Church, P. M., Matheson, J., Owens, B., & Grebe, C. (2018) · Laser radar technology and applications. Conference · doi:10.1117/12.2304348 · Cited by 16
Detecting drones using machine learning
Scheller, W. D. (2017) · doi:10.31274/etd-180810-5839 · Cited by 14
Empirical Study Of Drone Sound Detection In Real-Life Environment With Deep Neural Networks
Jeon, S., Kim, W.-H., Kwon, Y., Lee, Y.-J., Shin, J.-W., & Yang, H.-Y. (2018) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1159173 · Cited by 14
Digital Terrestrial Tracking: The Future of Surveillance
Wilkinson, G. R. (2014) · Cited by 12
Micro-Doppler Analysis of Small UAVs
Wit, J. J. M. D., Harmanny, R. I. A., & Prémel Cabic, G. (2012) · TNO Repository · Cited by 12
Statistical Machine Learning Algorithms for Target Classification from Acoustic Signature
Mirelli, V., Tenney, S. M., Bengio, Y., Chapados, N., & Delalleau, O. (2009) · Cited by 11
Mototolea, D., & Stolk, C. (2018) · International Conference on Communications (COMM) · doi:10.1109/iccomm.2018.8484827 · Cited by 8
Mototolea, D., & Stolk, C. (2018) · International Conference on Communications (COMM) · doi:10.1109/iccomm.2018.8430107 · Cited by 7
Knocking my neighbor's kid's cruddy drone offline.
Robinson, M., Mitchell, A., McGuire, R., & Herritage, K. (2015) · TIB AV-Portal · doi:10.5446/36427 · Cited by 6
Implementation and Performance Evaluation of Acoustic Denoising Algorithms for UAV
Chowdhury, A. S. K. (2020) · Digital Scholarship - UNLV (University of Nevada Reno) · doi:10.34917/10083129 · Cited by 4
Research works printed but not linked: 5
- Solodov, A., Williams, A., Hanaei, S., and Goddard, B. (2019, October 18). Analyzing the Threat of Unmanned Aerial Vehicles to Nuclear Facilities, SAND 2017-3408J, Available online: https://www.osti.gov/pages/servlets/purl/1356834.
- Skolnik, I.M. (1990). Radar Handbook, McGraw-Hill Education. [2nd ed.]. Available online: http://www.geo.uzh.ch/microsite/rsl-documents/research/SARlab/GMTILiterature/PDF/Skolnik90.pdf.
- Luo, A. (2016, January 4–7). Drones hijacking—multi-dimensional attack vectors and countermeasures. Presented at the DEF CON 24 Conference, Las Vegas, NV, USA. Available online: https://www.youtube.com/watch?v=R6RZ5KqSVcg.
- Rodday, N. (April, January 29). Hacking a professional drone. Proceedings of the Black Hat ASIA 2016, Singapore. Available online: https://www.blackhat.com/docs/asia-16/materials/asia-16-Rodday-Hacking-A-Professional-Drone.pdf.
- Vervisch-Picois, A., Samama, N., and Taillandier-Loize, T. (2017, January 14–17). Influence of GNSS spoofing on drone in automatic flight model. Proceedings of the ITSNT 2017: 4th International Symposium of Navigation and Timing. Ecole Nationale de L’aviation Civile (ENAC), Toulouse, France.
Cited by
Sorted by citations
A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones
Castrillo, V. U., Manco, A., Pascarella, D., & Gigante, G. (2022) · Drones · doi:10.3390/drones6030065 · Cited by 131
A Survey of Video Surveillance Systems in Smart City
Myagmar-Ochir, Y., & Kim, W. (2023) · Electronics · doi:10.3390/electronics12173567 · Cited by 134
Global Analysis of Active Defense Technologies for Unmanned Aerial Vehicle
Lyu, C., & Zhan, R. (2022) · IEEE Aerospace and Electronic Systems Magazine · doi:10.1109/maes.2021.3115205 · Cited by 85
Drone Detection and Tracking Using RF Identification Signals
Aouladhadj, D., Kpré, E., Deniau, V., Kharchouf, A., Gransart, C., & Gaquière, C. (2023) · Sensors · doi:10.3390/s23177650 · Cited by 84
Discussing resilience in the context of cyber physical systems
Colabianchi, S., Costantino, F., Gravio, G. D., Nonino, F., & Patriarca, R. (2021) · Computers & Industrial Engineering · doi:10.1016/j.cie.2021.107534 · Cited by 75
Metadata
- Authors
- Lykou, G., Moustakas, D., & Gritzalis, D.
- Affiliations
- Athens University of Economics and Business (GR)
- Corresponding author
- Gritzalis, Dimitris
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2020-06-22
- Volume
- 20
- Issue
- 12
- Pages
- 3537
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC7349857
- OpenAlex
- W3036046236
- Cited by
- 268
- References
- 78
- Citations by year
- 2026: 38 · 2025: 40 · 2024: 49 · 2023: 56 · 2022: 44 · 2021: 34 · 2020: 7
- FWCI
- 3.85
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › UAV Applications and Optimization
- Physical Sciences › Engineering › Aerospace Engineering › Air Traffic Management and Optimization
- Physical Sciences › Engineering › Aerospace Engineering › Military Technology and Strategies
- Keywords
- counter-UAS, unmanned aerial systems, airports, drone detection, drone identification, cyberattacks, critical infrastructure protection, attack scenarios, resilience planning, aviation security
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review