Detecting the undetectable: GAN-based strategies for network intrusion detection

Abstract

This study addresses the challenge of enhancing network security by proposing a novel intrusion detection system using Generative Adversarial Networks. Traditional intrusion detection system often fail to keep up with rapidly evolving cyber threats. Our approach integrates Generative Adversarial Networks to dynamically learn and adapt to both genuine and adversarial network traffic patterns. Using the KDD Cup 1999 dataset for validation, we design a sophisticated Generative Adversarial Network architecture with a generator and discriminator to improve the resilience of intrusion detection system. Our experimental results demonstrate the model’s effectiveness, evaluated through metrics such as F1 score, accuracy, precision, and recall. This research advances the state-of-the-art in cybersecurity by showcasing the potential of Generative Adversarial Networks to fortify intrusion detection system against evolving threats, underscoring the necessity for adaptive defense mechanisms in modern network security.