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Enhanced Primary User Emulation Attack Inference in Cognitive Radio Networks Using Machine Learning Algorithm

N. Sureka*, K. Gunaseelan

Anna University, College of Engineering Guindy, Chennai, India

* Corresponding Author: N. Sureka. Email:

Intelligent Automation & Soft Computing 2022, 34(3), 1893-1906.


Cognitive Radio (CR) is a competent technique devised to smart sense its surroundings and address the spectrum scarcity issues in wireless communication networks. The Primary User Emulation Attack (PUEA) is one of the most serious security threats affecting the performance of CR networks. In this paper, machine learning (ML) principles have been applied to detect PUEA with superior decision-making ability. To distinguish the attacking nodes, Reinforced Learning (RL) and Extreme Machine Learning (EML-RL) algorithms are proposed to be based on Reinforced Learning (EML). Various dynamic parameters like estimation error, attack detection efficiency, attack estimation rate, and learning rate have been examined with the Network Simulator 2 (NS2) tool.


Cite This Article

N. Sureka and K. Gunaseelan, "Enhanced primary user emulation attack inference in cognitive radio networks using machine learning algorithm," Intelligent Automation & Soft Computing, vol. 34, no.3, pp. 1893–1906, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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