Title: Enhancing network security in wireless communication systems using deep learning

Authors: Pratik Patel; N. Ashokkumar; S. Vidhya; Sheshang Degadwala

Addresses: National Forensic Sciences University, Police Bhavan Rd., Sector 9, Gandhinagar, Gujarat, 382007, India ' Department of Electronics and Communication Engineering, Erstwhile Sree Vidyanikethan Engineering College, Mohan Babu University, Tirupati, Andhra Pradesh, 517102, India ' Computer Science and Engineering Department, V.S.B. Engineering College, Karur, India ' Sigma University, Post Bakrol, Ajwa Road, Waghodia, Nimeta, Vadodara, Gujarat, 390019, India

Abstract: With the increasing reliance on digital connectivity, ensuring security in wireless communication systems has become more critical than ever. Traditional network intrusion detection systems (NIDS) primarily rely on pattern recognition techniques. However, they often struggle to detect novel threats effectively, leading to limitations in their performance. This study addresses these challenges by leveraging advancements in computing, networking, and deep learning methodologies. The proposed approach involves preprocessing and transforming network data to enhance its utility for threat detection. A deep learning-based NIDS was developed utilising a convolutional neural network (CNN) architecture. Key features were systematically selected, and dimensionality reduction techniques were applied to optimise the model's performance. The resulting system achieved a detection accuracy of 98.5%, demonstrating significant potential to outperform traditional methods. This approach represents a more robust and adaptive solution for addressing contemporary challenges in network security.

Keywords: network security; wireless communication; deep learning; IDS; intrusion detection system; CNN; convolutional neural network; WSNs; wireless sensor networks; CSI; channel state information; feature selection; PCA; principal component analysis.

DOI: 10.1504/IJMNDI.2026.152400

International Journal of Mobile Network Design and Innovation, 2026 Vol.12 No.1, pp.31 - 41

Received: 16 May 2025
Accepted: 07 Aug 2025
Published online: 18 Mar 2026 *

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