Intrusion detection method based on convolutional neural network and lightweight gradient elevator

A convolutional neural network and intrusion detection technology, applied in the field of network security, can solve the problems of low classification accuracy, high false alarm rate, company loss, etc., and achieve the effect of fast and accurate classification, high classification accuracy, and guaranteed classification effect.

Pending Publication Date: 2022-01-07
YANSHAN UNIV
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Problems solved by technology

In 2019, the hacker organization Fxmsp invaded three anti-virus companies, extracted the source code of anti-virus software, and caused huge losses to the companies

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  • Intrusion detection method based on convolutional neural network and lightweight gradient elevator
  • Intrusion detection method based on convolutional neural network and lightweight gradient elevator
  • Intrusion detection method based on convolutional neural network and lightweight gradient elevator

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[0042] The present invention will be further described below in connection with the examples below:

[0043] like Figure 1 to 9 FIG An intrusion detection based on convolutional neural network and lightweight hoist gradient, the limitation of the existing network intrusion detection algorithm when the imbalance data and complex high-dimensional data, and feature-based intrusion detection data the advantages of various technical methods, is proposed based on convolutional neural network (CNN, ConvolutionalNeural networks) and lightweight gradient elevator (LightGBM, Light gradient Boosting machine) intrusion detection method. The present invention is directed to detect intrusion classification, mainly consists of three parts: a data preprocessing, feature selection, classification intrusion detection.

[0044] First, in the process of introducing data preprocessed data type conversion, oversampling, and an image data converting method, adapted to make the data input format and equi...

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Abstract

The invention discloses an intrusion detection method based on a convolutional neural network and a lightweight gradient elevator, and relates to an intrusion detection technology in the field of network security, and the method comprises the steps: data preprocessing, wherein the data preprocessing process comprises data type conversion, an oversampling technology and image data conversion; feature selection: using a CNN model to select features; and intrusion detection classification: performing classification by using a Light GBM algorithm. According to the method, the difficulty of intrusion detection analysis caused by imbalance, high dimension and nonlinearity of data is overcome, so that the method shows better performance in multi-dimensional evaluation indexes such as accuracy, precision rate and missing report rate.

Description

technical field [0001] The present invention relates to intrusion detection technology in the field of network security, especially an intrusion detection method based on convolutional neural network and lightweight gradient boosting machine, which is used for processing complex unbalanced data and high-dimensional data limitations. Background technique [0002] With the development of network technology, the rapid growth of network scale not only brings convenience to people, but also brings risks and challenges. Problems such as the theft of private data and the encroachment of server resources have brought great troubles to people. In order to deal with many subsequent problems, intrusion detection technology came into being. For example, in 2017, hackers exploited Windows system vulnerabilities to cause 100,000 organizations around the world to be attacked by Bitcoin ransomware. In 2019, the hacker organization Fxmsp invaded three anti-virus companies, extracted the s...

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Application Information

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IPC IPC(8): G06F21/55G06K9/62G06N3/04G06N3/08
CPCG06F21/552G06N3/08G06N3/047G06N3/048G06N3/045G06F18/2415G06F18/241
Inventor 王倩赵文仿任家东
Owner YANSHAN UNIV
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