Muhammad Sohail, Muhammad Hamad and Anjum Saeed, University of Hertfordshire, United Kingdom

The rapidly evolving cybersecurity threats poses a significant challenge for traditional net-work intrusion detection systems. To tackle this issue, this project addresses the challenge of class imbalance in network intrusion detection by integrating a Conditional Generative Adversarial Network (CGAN) with a Convolutional Neural Network (CNN) classifier. The aim of this research is to generate synthetic attack samples to balance the dataset and improve detection accuracy by reducing the FNR. With the use of UNSW-NB15 dataset [20], the proposed model demonstrated high classification performance, achieving 99.45% accuracy with mini-mal false alarms. This research report discusses the system's methodology, experimentation evaluation metrics, development insights and highlights the potential of integrating generative data augmentation in cybersecurity.

Network Intrusion Detection, CGAN, CNN, Cybersecurity, Data Augmentation