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Hypergraph and random forest (HG-RF)-based intrusion detection method

A random forest, intrusion detection technology, applied in special data processing applications, instruments, electrical digital data processing and other directions, can solve the problems of low training accuracy and reduced voting accuracy, to improve classification accuracy, improve classification accuracy, reduce The effect of calculation

Active Publication Date: 2018-11-23
GUILIN UNIV OF ELECTRONIC TECH
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Problems solved by technology

However, since the final classification result of the random forest is determined by the voting of the decision trees it contains, this makes some of the decision trees with low training accuracy have the same voting ability, thereby reducing the accuracy of voting.

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  • Hypergraph and random forest (HG-RF)-based intrusion detection method
  • Hypergraph and random forest (HG-RF)-based intrusion detection method
  • Hypergraph and random forest (HG-RF)-based intrusion detection method

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Embodiment Construction

[0038] The specific implementation of the present invention will be further described below in conjunction with the drawings and examples, but it is not a limitation of the present invention.

[0039] figure 1 Shows an intrusion detection method based on hypergraph and random forest, including the following steps:

[0040] (1) Data preprocessing, the specific process is as follows: For the downloaded 10% KDD data set and corrected data set, use 10% of the KDD data set as the training set, and the corrected data set as the test set, and convert all character variables into For digital variables, use the standard deviation formula to standardize the data. The standard deviation formula is as follows:

[0041]

[0042] Where x i Represents the value of the i-th sample in each dimension attribute, μ represents the average value of each dimension feature attribute, and N represents the total number of training set samples;

[0043] (2) Feature screening to obtain a new feature subset F su...

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Abstract

The invention discloses a hypergraph and random forest (HG-RF)-based intrusion detection method, and belongs to the technical field of network intrusion detection. The method comprises the following steps: (1) carrying out data preprocessing; (2) carrying out feature screening to obtain a new feature subset; (3) calculating a Fisher score of each feature in each category, and carrying out descending-order arrangement; and (4) inputting a test sample set to a weighted random forest classifier to obtain final intrusion detection results of test samples. The method is based on a method of featurepreference, firstly carries out dimensional reduction processing on data, then carries out classification, improves intrusion detection speed and an accuracy rate of the classifier, and reduces a detection false-reporting rate.

Description

Technical field [0001] The present invention relates to the technical field of network intrusion detection, in particular to an intrusion detection method based on hypergraph and random forest (Hypergraph and Random forest, HG-RF). Background technique [0002] With the development of science and technology, the Internet has penetrated into all aspects of people's lives and work, leading to the generation and exchange of large amounts of information. How to ensure the security of user data information has become the focus of urgent research and solutions. Traditional methods include user authentication, access control, data encryption, firewalls, etc., but these passive defense technologies are no longer sufficient to resist the increasingly developed intrusion methods. [0003] Intrusion refers to an attempt to destroy confidentiality, integrity and availability or to bypass the security mechanism of a computer or network. Intrusion detection is the process of monitoring compute...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06F21/55
CPCG06F21/55
Inventor 江泽涛周谭盛子张少钦胡硕
Owner GUILIN UNIV OF ELECTRONIC TECH
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