AI-Driven Network Intrusion Detection System

Abstract

In the evolving landscape of network security, conventional Intrusion Detection Systems (IDS) often fall short in addressing sophisticated and novel cyber threats. It provides an advanced approach to Network Intrusion Detection by leveraging Generative Adversarial Networks (GANs) to enhance detection accuracy and adaptability. The proposed system integrates GANs to generate synthetic attack patterns and improve anomaly detection capabilities. By training a GAN with diverse network traffic data, our method not only detects known threats but also identifies previously unseen attack vectors with higher precision. We extend traditional IDS frameworks by incorporating the GAN's discriminator as the primary detection mechanism. This enhanced NIDS architecture demonstrates significant improvements in detecting zero-day attacks and evasion techniques compared to conventional signature-based and anomaly-based methods. The system is evaluated using standard network traffic datasets, such as NSL-KDD and CICIDS 2017, achieving superior performance metrics, including increased accuracy, precision, recall, and reduced false positives. Our approach provides a robust solution for modern network security, offering a scalable and adaptive mechanism to counteract evolving cyber threats. Future work will explore optimizing GAN architectures and integrating additional AI techniques to further bolster intrusion detection capabilities.