Enhancing DDoS Attack Detection in SDNs with GAN-Based Imbalanced Data Augmentation

Abstract

Securing computer networks has become crucial due to the ongoing emergence of diverse network attacks. The popularity of Software Defined Networks (SDN) has risen because of its ability to enhance network agility, efficiency, and adaptability to recent networking challenges. However, it is essential to note that SDNs, which depend on centralized controllers, can be severely affected by Distributed Denial of Service (DDoS) attacks. The threat of DDoS attacks has grown exponentially, resulting in the evolution of robust Machine Learning-based DDoS attack detection systems within SDN. DDoS attack detection systems may deliver poor performance when trained on imbalanced datasets. Traditional techniques for handling imbalanced datasets need to be revised. Recent advances in generative adversarial networks (GANs) have revealed significant potential in generating synthetic data while preserving the probability distribution of the original data. This innovative procedure offers a promising solution to mitigate the challenges of imbalanced data in DDoS attack detection. To address challenges originating from imbalanced training datasets, we employed Generative Adversarial models to generate adversarial attacks from one viewpoint and evaluate their quality from another perspective. We chose Generative Adversarial Networks (GANs), Bidirectional GANs (Bi-GANs), and Wasserstein GANs (WGANs) based on extensive usage and reliability criteria in various domains. We conducted a comprehensive assessment to evaluate their effectiveness and resilience in generating high-quality attacks. It helps to develop, train, and fine-tune machine and deep learning models to estimate their impacts. We utilized NSL-KDD and CICIDS-2017 datasets to ensure generalization, implementing both ML and DL approaches. The outcomes demonstrate that the WGAN model outperformed GAN, Bi-GAN, and the models trained on the original imbalanced dataset and traditional sampling techniques in binary and multiclass classifications for both datasets.