Industrial control intrusion detection system and method based on convolutional neural network architecture optimization

The invention discloses an industrial control intrusion detection system and method based on convolutional neural network architecture optimization. Historical monitoring data of a generation process are collected from a historical database of an industrial control system, the historical monitoring data are subjected to data analysis and normalization and then serve as an input data set of an industrial control intrusion detection offline training module, and a convolutional neural network architecture optimization platform based on a discrete population evolution method is designed; an industrial control intrusion detection feature library and a convolutional neural network model of an optimal architecture are obtained, and online detection of industrial control intrusion detection is realized for real-time monitoring data in a real-time database of the industrial control system. According to the invention, automatic generation and optimization design of a convolutional neural network architecture for an ind