Network intrusion detection method based on conditional variation auto-encoder

A network intrusion detection and self-encoder technology, applied in neural learning methods, biological neural network models, instruments, etc., can solve problems such as accuracy decline, and achieve the goal of enhancing generation ability, improving classification accuracy, and enhancing robustness. Effect

Active Publication Date: 2020-11-20
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

In order to solve the problem of accuracy drop caused by unbalanced samples in the network intrusion dataset, the conditional variational autoencoder is used as the generator of the entire conditional variational autoencoder-based network intrusion detection model for data augmentation

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  • Network intrusion detection method based on conditional variation auto-encoder
  • Network intrusion detection method based on conditional variation auto-encoder
  • Network intrusion detection method based on conditional variation auto-encoder

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[0043] Specific embodiments of the present invention will be described below in conjunction with the accompanying drawings, so that those skilled in the art can better understand the present invention. It should be noted that the described embodiments are some, not all, embodiments of the present invention, and are not intended to limit the scope of the claimed invention. All other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0044] Such as Figure 1-Figure 4 As shown in , a network intrusion detection method based on conditional variational autoencoder, uses the conditional variational autoencoder with logarithmic hyperbolic cosine as the loss function to expand the original data set, and then uses the expanded data set as The training set trains a classifier with a convolutional neural network as the main network structure, and uses the trained classifier for network intrus...

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Abstract

The invention discloses a network intrusion detection method based on a conditional variation auto-encoder, belongs to the technical field of network space intrusion detection, and the method comprises the steps: firstly training a conditional variation auto-encoder which takes a logarithmic hyperbolic cosine function as a loss function for the expansion of a data set, and then training a classifier based on the convolutional neural network through the data set after data expansion to serve as an intrusion detector of the whole model. According to the method, the classification detection performance on a network space intrusion data set is improved by utilizing the generation capacity of conditional variation self-encoding and the excellent feature extraction capacity of the convolutionalnetwork for data features, and meanwhile, the problem of performance reduction caused by imbalance of data set samples is also solved.

Description

technical field [0001] The invention belongs to the technical field of network space intrusion detection, and in particular relates to a network intrusion detection method based on a conditional variational autoencoder. Background technique [0002] Cyberspace intrusion detection technology refers to detecting the intrusion of a network device by obtaining the connection information, log files and other information of the device, that is, any action that attempts to destroy the data integrity, confidentiality and accessibility of the network device. With the continuous development of the Internet of Things and the increase of Internet of Things devices, how to ensure the security of devices in cyberspace has gradually become a research hotspot. Cyberspace intrusion detection technology aims to identify abnormal connection information of devices at the network layer of the Internet of Things network, so as to achieve the purpose of protecting user data, privacy and other info...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/241G06F18/214
Inventor 徐行李杰杨阳邵杰
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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