Semi-supervised intrusion detection method based on depth generation model

Abstract

The invention discloses a semi-supervised intrusion detection method based on a depth generation model. The method comprises the steps of: 1, preprocessing data: converting symbol attributes in a dataset into numerical attributes, and then normalizing all the numerical attributes; 2, converting high-dimensional feature representations of labeled and unlabeled data into low-dimensional representations of a new feature space by using the variational self-encoding technology in the generation model, adding a constraint to low-dimensional feature vectors to obey Gaussian positive distribution soas to obtain a hidden variable z, and training a classifier by using the hidden variable z in combination with a labeled sample; 3, reconstructing labeled sample data: jointly generating a new labeledsample by using the hidden variable z in combination with label class information; 4, reconstructing an unlabeled sample: predicting the probability of each class of an unlabeled sample by using thehidden variable z, and then generating a new unlabeled sample in combination with the hidden variable z; and 5, calculating a reconstruction error of the model with the newly generated labeled and unlabeled samples, and training and optimizing model parameters in combination with a classification error till convergence.

Description

technical field [0001] The invention is applied to the field of intrusion detection in network security. In particular, it concerns a semi-supervised intrusion detection method based on deep generative models. Background technique [0002] With the rapid development of network and information technology, network security has become a major concern. Intrusion Detection (Intrusion Detection) is a proactive security protection technology that detects intrusion behavior by analyzing network traffic or system audit records, and sends an alarm or takes defensive measures to ensure system security when suspicious communications are found. [0003] At present, the intrusion detection learning algorithm based on machine learning and deep learning is the focus of research by scholars at home and abroad. The summary and analysis of the existing intrusion detection methods are as follows: [0004] (1) Intrusion detection method based on statistics. The basis of the statistical model ...

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Problems solved by technology

[0008] Based on the above intrusion detection algorithms, they can usually only detect known attack types and require a large number of labeled samples, and the computational complexity of the model time is high.

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