A New Hybrid Adaptive Deep Learning-Based Framework for UAVs Faults and Attacks Detection-Reference-Cited by-同舟云学术

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A New Hybrid Adaptive Deep Learning-Based Framework for UAVs Faults and Attacks Detection

Published:2023-11 Issue:6 Volume:16 Page:4128-4139
ISSN:1939-1374
Container-title:IEEE Transactions on Services Computing
language:
Short-container-title:IEEE Trans. Serv. Comput.

Author:

Tlili Fadhila¹^ORCID,Ayed Samiha²^ORCID,Fourati Lamia Chaari³^ORCID

Affiliation:

1. National School of Electronics, Telecommunications of Sfax, Sfax, Tunisia

2. LIST3N-ERA, University of Technology of Troyes, Troyes, France

3. Digital Research Center of Sfax (CRNS), SM@RTS (Laboratory of Signals, systeMs, aRtificial Intelligence, neTworkS), Sfax University, Sfax, Tunisia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Information Systems and Management,Computer Networks and Communications,Computer Science Applications,Hardware and Architecture

Link

http://xplorestaging.ieee.org/ielx7/4629386/10360362/10237288.pdf?arnumber=10237288

Reference41 articles.

1. Investigation on vulnerabilities, threats and attacks prohibiting UAVs charging and depleting UAVs batteries: Assessments & countermeasures

2. Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges

3. Internet of Flying Things (IoFT): A Survey

4. Cloud-Based Cyber-Physical Intrusion Detection for Vehicles Using Deep Learning

5. Artificial Intelligence techniques to mitigate cyber-attacks within vehicular networks: Survey

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