Research on intrusion detection for maritime meteorological sensor network based on balancing generative adversarial network - Xin SUN; Guifu ZHANG; Hongyan XING; Wang Zenghui (Tongxin xuebao)
Aiming at the problem that the resources of maritime mobile terminals were limited and the network traffic was imbalanced in the MMSN (maritime meteorological sensor network) environment, which made it difficult to detect network intrusion accurately, a mobile edge computing based physical architecture of MMSN was proposed, and an intrusion detection model based on balancing generative adversarial network was proposed.First, an advanced balancing generative adversarial network was adopted to augment the imbalanced data.Then, a lightweight network based on group convolution was applied to intrusion data classification.Finally, compared with conventional data augmentation models, the computer simulation proves that the proposed model has a higher ability to recognize various attacks, especially minority class attacks on MMSN.