Optimal Deep Learning Empowered Malicious User Detection for Spectrum Sensing in Cognitive Radio Networks-Reference-Cited by-同舟云学术

Optimal Deep Learning Empowered Malicious User Detection for Spectrum Sensing in Cognitive Radio Networks

Published:2024 Issue: Volume:12 Page:35300-35308
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Almuqren Latifah¹^ORCID,Maray Mohammed²^ORCID,Alotaibi Faiz Abdullah³^ORCID,Alzahrani Abdulrahman⁴^ORCID,Mahmud Ahmed⁵,Rizwanullah Mohammed⁶

Affiliation:

1. Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi Arabia

2. Department of Information Systems, College of Computer Science, King Khalid University, Abha, Saudi Arabia

3. Department of Information Science, College of Humanities and Social Sciences, King Saud University, Riyadh, Saudi Arabia

4. Department of Computer Science and Engineering, College of Computer Science and Engineering, University of Hafr Al Batin, Hafr Al Batin, Saudi Arabia

5. Research Center, Future University in Egypt, New Cairo, Egypt

6. Department of Computer and Self Development, Deanship of Preparatory Year, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia

Funder

Deanship of Scientific Research at King Khalid University for funding this work through large group Research Project

Princess Nourah bint Abdulrahman University Researchers Supporting Project

Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

Research Supporting

King Saud University, Riyadh, Saudi Arabia

via funding from Prince Sattam bin Abdulaziz University

Future University in Egypt

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10440279.pdf?arnumber=10440279

Reference21 articles.

1. Machine-Learning-Assisted Cyclostationary Spectral Analysis for Joint Signal Classification and Jammer Detection at the Physical Layer of Cognitive Radio

2. Extreme Learning Machine Based Identification of Malicious Users for Secure Cooperative Spectrum Sensing in Cognitive Radio Networks

3. A Survey on Machine Learning Algorithms for Applications in Cognitive Radio Networks

4. Energy-efficient adaptive sensing for cognitive radio sensor network in the presence of primary user emulation attack;Ram;Comput. Electr. Eng.,2023

5. Research on Key Technology of Full Duplex Cognitive Radio Network

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An Emergent Self-Attention LSTM Module-Based Malicious User Detection in Cognitive Radio Networks;Advances in Wireless Technologies and Telecommunication;2024-06-14