Security Enhancement in 5G Networks by Identifying Attacks Using Optimized Cosine Convolutional Neural Network
Year: 2025
Type: article
Abstract: ABSTRACT The exponential growth of 5G networks has introduced advanced capabilities but also heightened susceptibility to sophisticated cyberattacks. To address this, a robust and optimized security framework is proposed, leveraging a Cosine Convolutional Neural Network (CCNN) for attack detection. By emphasizing angular correlations in data, the CCNN improves feature extraction by substituting cosine similarity‐based adjustments for conventional convolution processes. To maximize the CCNN's performance, the Exponential Distribution Optimizer (EDO) is employed optimize CCNN. The optimal configuration of CCNN is achieved using EDO's probabilistic search mechanism, which is inspired by exponential distribution and helps to maintain a balanced exploration‐exploitation strategy. This integrated approach significantly improves detection accuracy, robustness, and scalability while maintaining low computational overhead. Comprehensive evaluations demonstrate the model's efficacy in identifying diverse attack patterns in 5G networks, outperforming conventional methods. The proposed framework establishes a new benchmark for secure, intelligent 5G infrastructures, contributing to the advancement of cybersecurity in next‐generation networks. The introduced approach attains higher accuracy of 99%.
Source: Internet Technology Letters
DOI: 10.1002/itl2.70003
Authors: Premalatha Santhanamari, Vijayakumar Kathirgamam, Lakshmisridevi Subramanian, Thamaraikannan Panneerselvam, Rathish Chirakkal Radhakrishnan
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4408289516