A Multi Stage Data Attack Traceability Method Based on Convolutional Neural Network for Industrial Internet

Abstract

In order to accurately define the network area to which data attacks belong and avoid multi-stage delay in industrial Internet, a multi-stage data attack traceability method based on convolutional neural network is proposed for industrial Internet. The convolution neural network is used to solve the training expression of the classifier. Combined with the multi-stage attack data and information samples of the industrial Internet, improve the expression conditions of the encryption algorithm, and realize the construction of the multi-stage consensus mechanism of the industrial Internet. Define the value range of multi-stage data of workflow meta industrial internet, so as to determine the function of the traceability automatic capture mechanism on data samples, and complete the traceability of multi-stage data attacks of industrial internet. The comparative experiment results show that the proposed method can accurately define the sample interval of data attack behavior in the six network regions selected in this experiment, and has strong practical value in solving the multi-phase delay problem of industrial Internet.