Enhancing network security in wireless communication systems using deep learning

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.