Reinforcing Cybersecurity with GAN-Enabled Intrusion Detection

Abstract

In the realm of cybersecurity, Intrusion Detection Systems (IDS) are essential tools for identifying network attacks. While traditional machine learning algorithms have been widely used in security, they've struggled to keep pace with evolving technology and the challenges of modern cyber threats. This has led to a gradual decline in the effectiveness of machine learning-based intrusion detection systems. However, there's hope on the horizon in the form of Generative Adversarial Networks (GANs). GANs have garnered attention for their ability to effectively detect anomalies in complex, high-dimensional data. By leveraging deep learning techniques, we can address the shortcomings of traditional machine learning algorithms in intrusion detection. This study proposes to explore the use of GANs and their variations for network intrusion detection using real-world datasets. The aim is to demonstrate the feasibility of this approach and provide comparative results to evaluate its effectiveness.