Key information infrastructure asset identification method combined with mixed random forest

A random forest and infrastructure technology, applied in character and pattern recognition, resources, computer parts, etc., can solve the problem of inability to identify and identify key information infrastructure assets, and improve generalization ability, accuracy and strong scientificity. , the effect of improving precision and recall

Active Publication Date: 2019-09-17
BEIJING INSTITUTE OF TECHNOLOGYGY
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0009] To sum up, due to the large number and variety of critical information infrastructure assets, identification methods based on artificial calibration or rule matching cannot quickly, comprehensively and accurately identify critical information infrastructure assets

Method used

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  • Key information infrastructure asset identification method combined with mixed random forest
  • Key information infrastructure asset identification method combined with mixed random forest
  • Key information infrastructure asset identification method combined with mixed random forest

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Embodiment Construction

[0034] In order to better illustrate the purpose and advantages of the present invention, the implementation of the method of the present invention will be further described in detail below in conjunction with examples.

[0035] The specific process is:

[0036] Step 1: Structuralize the collected facility asset data and optimize the expression of features to obtain extended feature vectors.

[0037] Step 1.1, clean and optimize the collected infrastructure equipment logs, network traffic and other complex data to obtain original asset data

[0038] Step 1.2, based on the four aspects of keywords, time period nodes, and geographic associations, extract the four feature vectors of keyword feature vectors, time period feature vectors, behavior feature vectors, and geographic feature vectors from the original asset data.

[0039] In step 1.3, the four feature vectors are normalized, standardized and dummy variable processed, and sequentially spliced ​​to obtain high-dimensional ...

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Abstract

The invention discloses a key information infrastructure asset identification method combined with a mixed random forest, and belongs to the technical field of computers and information science. The method comprises the following steps of carrying out the structured processing on the collected facility asset data and carrying out the feature optimization expression to obtain an extended feature vector; in combination with a Delphi expert consultation method and a principal component analysis method, analyzing the key influence factors of the asset facilities, and extracting the key feature vectors; combining the plurality of random forest judgment models with a gating function to obtain a mixed random forest judgment model; and based on the constructed mixed random forest model, identifying whether the traffic is a key asset infrastructure or not. According to the key information infrastructure asset recognition method provided by the invention, the asset feature construction and the key factor extraction are realized by combining a machine learning method under big data, and the respective expert models are constructed by partitions, so that the recognition accuracy and efficiency are improved, and the generalization ability and expandability of the model are improved.

Description

technical field [0001] The invention relates to a key information infrastructure asset identification method combined with a mixed random forest, which belongs to the technical field of computer and information science. Background technique [0002] Critical information infrastructure plays a pivotal role in national economic and social development and is an important national strategic asset. Therefore, many countries attach great importance to the protection of critical information infrastructure, and identifying whether the traffic is critical information infrastructure is a prerequisite for its security protection, and it is also one of the difficulties in practice. So far, the industry's identification methods for critical information infrastructure are mainly based on artificial calibration or rule matching identification methods, which have achieved certain results, but there are still many problems. [0003] 1. Identification method based on artificial calibration ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06Q10/06G06Q50/26
CPCG06Q10/0639G06Q50/26G06F18/2411G06F18/24323
Inventor 罗森林门元昊潘丽敏陈传涛秦枭喃
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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