Okwudili Onyishi

Work place: Department of Electrical and Electronics Engineering, Federal University of Technology Minna, Nigeria

E-mail: okwudilionyishi653@gmail.com

Website: https://orcid.org/0000-0002-7830-4771

Research Interests: Artificial Intelligence, Data Mining, Data Structures and Algorithms, Mathematics of Computing, Models of Computation

Biography

Okwudili Onyishi is a first-class student of Electrical and Electronic Engineering department, and a research assistant at Green Wireless Networking (GreenWiN) research group, Federal University of Technology Minna, Nigeria. He is a recipient of various scholarship awards such as Agbami Petroleum and Petroleum Development Trust Fund (PTDF) Nigeria scholarships. His research interests include artificial intelligence, data mining, internet of things and cloud computing.

Author Articles
A Technique for PUE Detection and Isolation in Cognitive Radio Network

By Samuel A. Adebo Elizabeth N. Onwuka Abraham U. Usman Supreme Ayewoh Okoh Okwudili Onyishi

DOI: https://doi.org/10.5815/ijwmt.2023.03.02, Pub. Date: 8 Jun. 2023

The primary aim of a cognitive radio (CR) system is to optimize spectrum usage by exploiting the existing spectrum holes. Nevertheless, the success of cognitive radio technology is significantly threatened by the primary user emulation attack (PUEA). A rogue secondary user (SU) known as the primary user emulator (PUE) impersonates a legitimate primary user (PU) in a PUEA, thereby preventing other SUs from accessing the spectrum holes. Which leads to the decrease in quality of service (QoS), connection undependability, degraded throughput, energy depletion, and the network experiences a deterioration in its overall performance. In order to alleviate the impact of PUEA on Cognitive Radio Networks (CRNs), it is necessary to detect and isolate the threat agent (PUE) from the network. In this paper, a method for finding and isolating the PUE is proposed. MATLAB simulation results showed that the presence of PUE caused a significant decrease in the throughput of SUs, from to . The throughput was highest at a false alarm (FA) probability of 0.0, indicating no PUE, and decreased as the FA probability increased. At a FA probability of 1, the throughput reached zero, indicating complete takeover of the spectrum by PUE. By isolating the PUE from the network, the other SUs can access the spectrum holes, leading to increased QoS, connection reliability, improved throughput, and efficient energy usage. The presented technique is an important step towards enhancing the security and reliability of CRNs.

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