Anomaly-based network intrusion detection using denoising autoencoder and Wasserstein GAN synthetic attacks

Abstract

Intrusion detection systems face challenges in handling high-dimensional, large-scale, and imbalanced network traffic data. This paper proposes a novel architecture combining a denoising autoencoder (AE) and a Wasserstein generative adversarial network (WGAN) to address these issues. The AE-WGAN model extracts high-representative features and generates realistic synthetic attacks, effectively resolving data imbalance and enhancing anomaly-based intrusion detection. Extensive experiments on NSL-KDD and CICIDS-2017 datasets, using both binary and multiclass classification scenarios with various classifier architectures, demonstrate the model’s superior performance. The proposed approach outperforms state-of-the-art models in accuracy, precision, recall, and F1 score, showing excellent generalization capabilities against unseen attacks. Time complexity analysis reveals computational efficiency while maintaining high-quality synthetic attack generation. This research contributes a robust, efficient, and adaptable framework for intrusion detection, capable of handling modern network traffic complexities and evolving cyber threats.