GAI-Enhanced Intrusion Detection using Began-GRU Framework to Address Data Imbalance

With the developing communication technologies, the networks become more complicated and data is raised, which expands the surface of threats and increases the security risks. However, due to evolution of modern network environments vulnerability analysis is a difficult task and traditional Network Intrusion Detection Systems have problems in detection of advanced and new threats. One Cardinal restriction is the data imbalance: normal traffic mete out malicious traffic to such extent that machine learning models are not capable of predicting less observed classes of attack This work proposes a new intrusion detection model by integrating Boundary Equilibrium Generative Adversarial Networks with Gated Recurrent Units to help solve this problem. The integration of generative methods to invent images that mimic real images and temporal sequence methods enhances the quality and balance of training images. It was discovered that this new approach significantly increased the classification accuracy with regard to the detection of some specific attack methods, thereby enhancing the performance of the intrusion detection system.