Deep learning method for intelligent intrusion detection system

Abstract

In the field of cybersecurity, the threat landscape continues to evolve, requiring innovative approaches to effectively detect intrusions. This study explores the application of deep learning methods in intelligent intrusion detection systems and proposes a new method using conditional transformation generative adversarial networks (CTGAN). The proposed deep learning model integrates CTGAN into an intrusion detection framework, enabling the creation of diverse and representative synthetic samples to enhance the training dataset. This extension is designed to improve the model’s ability to detect subtle deviations that indicate an intrusion attempt. We evaluate the effectiveness of the CTGAN-based approach by conducting extensive experiments on benchmark datasets and demonstrate its superiority in identifying complex intrusion patterns.