An enhanced BiGAN architecture for network intrusion detection

Abstract

Intrusion detection systems face significant challenges in handling high-dimensional, large-scale, and imbalanced network traffic data. This paper proposes a new architecture combining a denoising autoencoder (AE) and a Wasserstein Generative Adversarial Network (WGAN) to address these challenges. The AE-WGAN model extracts high-representative features and generates realistic synthetic attacks, effectively resolving data imbalance and enhancing anomaly-based intrusion detection. Our extensive experiments on NSL-KDD and CICIDS-2017 datasets demonstrate superior performance, achieving 98% accuracy and 99% F1-score in binary classification, surpassing recent approaches by 7%–15%. In multiclass cases, the model achieves 89% precision for DoS attacks and 84% for Probe attacks, while maintaining 79% precision for rare U2R attacks. Time complexity analysis reveals 23% reduced training time while maintaining high-quality synthetic attack generation, contributing a robust framework capable of handling modern network traffic complexities and evolving cyber threats.