Journal of Modern Technology and Engineering

This paper delves into the realm of enhancing wireless security through the application of deep learning techniques for network attack detection and advanced classification. By leveraging the power of machine learning algorithms, particularly deep neural networks, the study aims to address the evolving challenges of securing wireless networks. The research explores the utilization of generative adversarial networks to generate synthetic features for analyzing Wi-Fi signal quality, as well as the development of hybrid security analysis systems combining model-checking techniques and deep learning for malware detection in IoT applications. Furthermore, the paper investigates the effectiveness of deep learning models in predicting cyber-attacks and detecting network intrusions, emphasizing the importance of combining traditional machine learning approaches with deep learning for improved classification of network attacks. Through a comprehensive review of existing literature and research directions, the study highlights the potential of deep learning for fortifying wireless security and offers insights into future advancements in this critical domain.