Multiple Features-Aided Malicious Users Detection in OFDM-based Cognitive Radio-Internet of Things-Reference-Cited by-同舟云学术

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Multiple Features-Aided Malicious Users Detection in OFDM-based Cognitive Radio-Internet of Things

Published:2023-06-16 Issue: Volume: Page:
ISSN:
Container-title:2023 International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM)
language:
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Author:

Miah Md Sipon¹,Rahman Md. Mahbubur¹,Niu Mingbo²,Armada Ana Garcia³

Affiliation:

1. Islamic University,Department of Information and Communication Technology,Kushtia,Bangladesh,7003

2. Chang'an University,Internet of Vehicle2 to Road Research Laboratory,Shaanxi,China,710018

3. Universidad Carlos III de Madrid, Leganes,Department of Signal Theory and Communications,Madrid,Spain,28911

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10211113/10212433/10212783.pdf?arnumber=10212783

Reference23 articles.

1. Support Vector Machine-Based Classification of Malicious Users in Cognitive Radio Networks

2. Primary user emulation and jamming attack detection in cognitive radio via sparse coding

3. Throughput Maximization for Secondary User Under Battery Imperfections in Cognitive Radio Networks

4. Support vector machine approach of malicious user identification in cognitive radio networks

5. Machine learning-based malicious user detection for reliable cooperative radio spectrum sensing in Cognitive Radio-Internet of Things

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