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

Author:

Almuqren Latifah1ORCID,Maray Mohammed2ORCID,Alotaibi Faiz Abdullah3ORCID,Alzahrani Abdulrahman4ORCID,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)

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

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