SE-IDS: A Sample Equalization Method for Intrusion Detection in Industrial Control System

With the continuous emergence of cyber attacks, the security of industrial control system (ICS) has become a hot issue in academia and industry. Intrusion detection technology plays an irreplaceable role in protecting industrial system from attacks. However, the imbalance between normal samples and attack samples seriously affects the performance of intrusion detection algorithms. This paper proposes SE-IDS, which uses generative adversarial networks (GAN) to expand the minority to make the number of normal samples and attack samples relatively balanced, adopts particle swarm optimization (PSO) to optimize the parameters of LightGBM. Finally, we evaluated the performance of the proposed model on the industrial network dataset.