Deep Learning Techniques for Network Security

Abstract

This article explores the seven outstanding deep-learning techniques used to enhance network security. It provides a comprehensive analysis of how these techniques address various cybersecurity challenges, including intrusion detection, malware classification, and anomaly detection. This review highlights the effectiveness of deep learning models such as Convolutional Neural Networks (Recurrent neural networks (RNNs) and automatic encoders used in processing large datasets and identifying complex patterns representing security threats. The article also discusses the advantages and limitations of each technique, emphasizing the importance of feature extraction, model training, and real-time processing capabilities. By combining the findings of the current research, this review aims to guide future research and practical implementation of deep learning in securing network infrastructure against evolving cyber threats. The review provided a comprehensive summary of the deep learning techniques used in network security, highlighting their strengths and limitations. The findings showed that deep learning has significant potential to improve detection and response to network threats, although challenges related to model interpretability, data quality, and computational efficiency should be addressed.