A Survey on the Use of Deep Learning Techniques for UAV Jamming and Deception

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG43__%2F23%3A00558299" target="_blank" >RIV/60162694:G43__/23:00558299 - isvavai.cz</a>
Alternative codes found
RIV/00216305:26220/22:PU145682
Result on the web
<a href="https://www.mdpi.com/2079-9292/11/19/3025" target="_blank" >https://www.mdpi.com/2079-9292/11/19/3025</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/electronics11193025" target="_blank" >10.3390/electronics11193025</a>

Result language
angličtina
Original language name
A Survey on the Use of Deep Learning Techniques for UAV Jamming and Deception
Original language description
Unmanned aerial vehicles (UAVs) can be used for a variety of illegal activities (e.g., industrial espionage, smuggling, terrorism). Given their growing popularity and availability, and advances in communications technology, more sophisticated ways to disable these vehicles must be sought. Various forms of jamming are used to disable drones, but more advanced techniques such as deception and UAV takeover are considerably difficult to implement, and there is a large research gap in this area. Currently, machine and deep learning techniques are popular and are also used in various drone-related applications. However, no detailed research has been conducted so far on the use of these techniques for jamming and deception of UAVs. This paper focuses on exploring the current techniques in the area of jamming and deception. A survey on the use of machine or deep learning specifically in UAV-related applications is also conducted. The paper provides insight into the issues described and encourages more detailed research in this area.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
<a href="/en/project/TM02000035" target="_blank" >TM02000035: NEO classification of signals (NEOCLASSIG) for radio surveillance systems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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