Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

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
View PDF7 Cites 17 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

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

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • 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

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[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...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

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...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
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
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com