DEGAN : Towards botnet detection in IIoT with dual-enhanced GAN under imbalanced data

This research paper proposes DEGAN, a machine learning approach using dual-enhanced GAN (generative adversarial network, a type of AI that learns by having two competing neural networks) to detect botnets (networks of infected computers controlled remotely) in IIoT (industrial internet of things, devices like sensors and machines in factories connected to the internet). The method addresses the challenge of imbalanced data, where there are far fewer examples of botnet attacks than normal network activity, which makes training detection systems difficult.