Research on data imbalance in intrusion detection using CGAN

To address the problems of attack category omission and poor generalization ability of traditional Intrusion Detection System (IDS) when processing unbalanced input data, an intrusion detection strategy based on conditional Generative Adversarial Networks (cGAN) is proposed. The cGAN generates attack samples that approximately obey the distribution pattern of input data and are randomly distributed within a certain bounded interval, which can avoid the redundancy caused by mechanical data widening. The experimental results show that the strategy has better performance indexes and stronger generalization ability in overall performance, which can solve insufficient classification performance and detection omission caused by unbalanced distribution of data categories and quantities.