Title: Detection of primary user emulation attack using the share and hunt optimisation based deep CNN classifier

Authors: Asmita A. Desai; Pramod B. Patil

Addresses: Electronics & Telecommunication Engg., Dr. Rajendra Gode Institute of Technology & Research, Amravati Mardi Road, Amravati, Maharashtra, 444602, India ' Electronics & Telecommunication Engg., Sant Gadge Baba Amravati University, Camp Area, Near Tapovan Gate, Amravati, Maharashtra, 444602, India

Abstract: In this research, the share and hunt optimisation-based deep classifier is developed for accurate primary user emulation attack (PUEA) detection, which improvise the efficiency of utilisation. The detection of primary user emulation attacks and enhancing the primary user performance is done by the three-layered approach in the cognitive radio network. The proposed method investigates the malicious user actions in the CR network, which prevents interference involved in the primary user. The performance of the proposed three-layered approach using the share and hunt optimisation based deep convolutional neural network (CNN) classifier is evaluated using the parameters, such as detection rate, delay, and throughput, and the analysis is performed using the Rayleigh and the awgn channel in the CR environment. The detection rate and the throughput of the attack detection are highly accurate and the delay is rapid for the developed method. In imminent, the protection of the CR network is highly improved with other enhanced approaches.

Keywords: cognitive radio network; deep CNN; PUEA detection; optimisation; secured spectrum sensing.

DOI: 10.1504/IJAACS.2025.144264

International Journal of Autonomous and Adaptive Communications Systems, 2025 Vol.18 No.1, pp.23 - 44

Received: 24 Jun 2022
Accepted: 18 Oct 2022
Published online: 04 Feb 2025 *

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