Bilateral feature-based turbine network intrusion detection method based on bidirectional generative adversarial network

The invention discloses a turbine network intrusion detection method based on a bidirectional generative adversarial network of bilateral features, which combines the physical side data and network side data of a turbine, and adopts a bidirectional generative adversarial network algorithm to judge whether a network intrusion behavior exists in a turbine system. The method comprises the following steps: screening and extracting physical side and network layer features by adopting a data analysis method to construct an intrusion detection feature set; generating a sliding index average bidirectional generative adversarial network based on the generative adversarial network; training a bidirectional generative adversarial network based on the intrusion detection feature set; and detecting the steam turbine network intrusion behavior based on the trained bidirectional generative adversarial network. The steam turbine network intrusion detection accuracy obtained through the method is high, potential network attac