An Efficient Methodology for Detecting Malicious Nodes in Cognitive Radio Networks
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Computer Science Applications
Link
https://link.springer.com/content/pdf/10.1007/s11277-023-10603-0.pdf
Reference15 articles.
1. Liu, L., Yang, J. & Meng, W. (2019). Detecting malicious nodes via gradient descent and support vector machine in internet of Things. Computers & Electrical Engineering, 77, 339–353.
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3. Chakraborty, S., Nandi, S., & Chattopadhyay, S. (2016). Alleviating hidden and exposed nodes in high-throughput Wireless Mesh Networks. IEEE Transactions on Wireless Communications, 15(2).
4. Wu, R., Deng, X., Lu, R., & Xuemin, S. (2015). Trust-Based Anomaly Detection in Emerging Sensor Networks. International Journal of Distributed Sensor Networks, 11,Article ID 363569.
5. Sajjada, S. M., Boukb, S. H., & Yousaf, M. (2015). Neighbor Node Trust based intrusion detection system for WSN. Procedia Computer Science, 63, 183–188.
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1. OntoBlock: a novel ontological-based and blockchain enabled spectrum sensing framework for detection of malicious users in cognitive radio internet of things (CR-IoT) networks;International Journal of Information Technology;2024-06-21
2. Experimental testing and analysis of regression algorithms for spectrum sensing in cognitive radio networks;Wireless Networks;2024-05-14
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