The network data in the current network environment is enormous

complex

and multidimensional

which is hugely different from the past. The traditional machine learning method needs to manually extract a large number of features in the face of complex high-dimensional data

and the process is complex and computationally intensive

which is not conducive to the current real-time and accuracy requirements of intrusion detection. Thus

in order to reduce the data dimension and eliminate redundant information

an intrusion detection method based on DAN-BP which combines deep auto-encoder network (DAN) and BP algorithm is proposed. First

a DAN model was constructed by overlaying several auto-encoder networks

and the network feature data was used as the input of the model

which enables the model to intelligently extract the distribution rules of the network data layer by layer

thereby obtaining a new low-dimensional feature data set. Then the low-dimensional data was classified and identified by the BP algorithm. In this research

the regularization correction was added to the auto-encoder network to prevent the trained auto-encoder network from directly copying the input information and influencing the training effect. Moreover

noise was added to the input data

and the reconstruction error of the original data and the output data was learned to achieve the purpose of denoising so that the learned new feature data is more robust. The traditional dimensionality reduction method and the proposed intrusion detection method were compared in this paper. Results show that the proposed method has better performance in classification accuracy

false alarm rate

and detection rate.