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Network security situation evaluation method based on deep self-encoding neural network model

A neural network model and network security technology, applied in the field of network security situation assessment based on the deep self-encoded neural network model, can solve the problems of network threat attacks, unable to meet real-time and intuitive assessment requirements, and achieve the goal of improving traffic detection rate Effect

Active Publication Date: 2020-12-04
CIVIL AVIATION UNIV OF CHINA
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AI Technical Summary

Problems solved by technology

[0003] With the popularization of the network and the wide application of big data, the network has been attacked by a large number of network threats. Therefore, the traditional network security situation assessment method has been unable to meet the needs of real-time and intuitive assessment.

Method used

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  • Network security situation evaluation method based on deep self-encoding neural network model
  • Network security situation evaluation method based on deep self-encoding neural network model
  • Network security situation evaluation method based on deep self-encoding neural network model

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Embodiment Construction

[0045] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, but the following embodiments in no way limit the present invention.

[0046] Such as figure 1 As shown, the network security situation assessment method based on the deep self-encoding neural network model provided by the present invention includes the following steps carried out in order:

[0047] 1) The S1 stage of constructing the deep self-encoder neural network model: construct as figure 2 The shown deep autoencoder neural network (AEDNN) model composed of deep autoencoder (DAE) and deep neural network (DNN); Divided into normal traffic and abnormal traffic, and can also specifically divide network traffic into various types of traffic;

[0048] 2) The S2 stage of obtaining network traffic data: select the relatively authoritative NSL-KDD intrusion data set in the field of network security as the evaluation data set; the NSL-KDD intrusio...

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Abstract

The invention discloses a network security situation assessment method based on a deep self-encoding neural network model. The method comprises the following steps: constructing a deep self-encoding neural network model; acquiring network flow data; preprocessing the data; resampling the data; training a deep self-encoding neural network model; testing a deep self-encoding neural network model; and quantitatively evaluating the network security situation. The deep self-encoding neural network model provided by the invention can detect the abnormal flow in the network, and in addition, the provided undersampling and oversampling weighting algorithm can improve the flow detection rate with less data volume. Based on the network security situation value calculated by the method, the state ofthe current network can be more intuitively and accurately expressed, and decision opinions can be provided, so that a network manager can more comprehensively know the network situation.

Description

technical field [0001] The invention belongs to the technical field of network information security, and in particular relates to a network security situation assessment method based on a deep self-encoding neural network model. Background technique [0002] With the rapid development of various network technologies, the security problems brought about by them are also becoming more and more prominent. Network security issues have brought serious harm to people's privacy and life, especially in the big data environment, people can no longer leave the network, so the network security issues they face are very serious. Although various network security protection measures have been taken, the impact of various factors on the network environment has not been considered comprehensively, so the requirements for comprehensively obtaining network status cannot be met. Network security situation assessment is a commonly used and effective solution. It can understand the network sec...

Claims

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Application Information

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IPC IPC(8): H04L29/06G06N3/04
CPCH04L63/1416H04L63/1425G06N3/045
Inventor 杨宏宇曾仁韵谢丽霞
Owner CIVIL AVIATION UNIV OF CHINA