AI Balancer: Tackling NIDS Dataset Imbalance with Generative AI

  Introduction In the era of digital transformation, Network Intrusion Detection Systems (NIDS) play a crucial role in maintaining cybersecurity. However, one significant challenge in developing effective NIDS is the imbalance in network traffic datasets. In this blog post, we'll explore how Conditional Tabular Generative Adversarial Networks (CTGAN) can address this issue by generating synthetic data to balance the dataset, thereby improving the performance of NIDS. Understanding the Imbalance Problem Network traffic datasets often exhibit a heavy imbalance, with benign traffic far outnumbering malicious traffic. This imbalance can lead to biased models that struggle to accurately detect intrusions, as they are trained predominantly on benign samples. Traditional resampling techniques like SMOTE and undersampling have limitations in capturing the complex patterns of network traffic. Introducing CTGAN CTGAN, developed by YData, is a powerful tool designed to generate high-quality s...