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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/00216305:26220/22:PU145682

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/TM02000035" target="_blank" >TM02000035: Pokroková klasifikace signálů (NEOCLASSIG) pro radio-průzkumné systémy</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

ELECTRONICS

ISSN

2079-9292

e-ISSN

2079-9292

Svazek periodika

11

Číslo periodika v rámci svazku

19

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

32

Strana od-do

3025

Kód UT WoS článku

000866677900001

EID výsledku v databázi Scopus

2-s2.0-85139826714