Abstract: Aiming at the difficulties that training samples of intrusion detection algorithms based on supervised learning are insufficient,and unsupervised algorithms have low detection rate,a new semi-supervised intrusion detection method based on deep generative models was proposed.This method aims to improve the detection accuracy and the generalization ability of the model by constructing an effective objective function.First,variational auto-encoder in the model is employedto map the vector of raw data from the high-dimensional space to low-dimensional,and the corresponding optimal low-dimension representation of raw can be obtained.Then,the generative model is used to improve the classification accuracy by only using the labeled samples.Experiments show that this method can achieve high accuracy while using a limited number of labeled samples.

Key words: Generative model, Intrusion detection, Semi-supervised, Variational autoencoder