Network Anomaly Detection Using Generative Adversarial Networks

In the era of smart devices connected to the internet, cybersecurity is becoming riskier, especially from attacks from within. Finding unusual network traffic is important, and the NSL-KDD dataset is known for its large amount of network data. It’s a good starting point. The goal of this research is to develop an anomaly detection system based on adversarial generative networks (GANs) that can identify anomalous network usage patterns that can indicate potential intrusion. Unlike traditional techniques, GAN offers a flexible, data-driven approach to cybersecurity that can dynamically adapt to new threats with an astounding purity of 96%, including deep learning models ($90-94 \%$) and unsupervised machine learning (${9 2 \%}$) algorithms, hybrid genetic methods (88%), and classical NSL-KDD-based methods (83%). This suggests that GAN models are a potential alternative because they provide remarkable improvements in anomaly detection for network security.