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

Outlier asset detection method and system based on machine learning

A machine learning and detection method technology, applied in the field of data processing, can solve the problems of monitoring blind spots, equipment assets cannot be detected in time and other problems, to achieve the effect of fast execution, improved usability and security, and simple process

Inactive Publication Date: 2021-11-12
中孚安全技术有限公司
View PDF4 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Although traditional monitoring software does play a certain role in discovering equipment failures and abnormal operation, the existing monitoring software can only detect equipment assets that are faulty and abnormally operating, and cannot detect abnormalities in time when the equipment assets are in normal operation. , there are obvious monitoring blind spots

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
  • Outlier asset detection method and system based on machine learning
  • Outlier asset detection method and system based on machine learning
  • Outlier asset detection method and system based on machine learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0057] Specific embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0058] like figure 1 A method for detecting outlier assets based on machine learning is shown, including the following steps:

[0059] S1: Preprocess the basic information of assets to form asset statistics and store them in the asset statistics table.

[0060]Among them, the basic asset information includes: asset information, asset vulnerability information, asset alarm information and asset access information. First, associate asset information, asset vulnerability information, asset warning information, and asset access information based on the asset ID; then, group and aggregate asset statistics based on the asset ID and asset status; finally, store the asset statistics in the asset statistics table .

[0061] The data structure and sample of asset statistics information are briefly described as follows: {statistical date: 20210804, asset ID: Z...

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 provides an outlier asset detection method and system based on machine learning. The method comprises the steps of carrying out the preprocessing of asset basic information, forming asset statistical information, and storing the asset statistical information in an asset statistical table, taking the asset statistical information as input information of an outlier asset algorithm model, and applying the outlier asset algorithm model to generate outlier asset information which does not belong to any cluster, and writing the outlier asset information into an outlier asset table. According to the method, the to-be-detected asset data set can be aggregated through the clustering algorithm based on machine learning, the asset objects not belonging to any cluster are judged as the outliers in the asset data set, so that the outlier assets are obtained, and the asset risk is removed in time in combination with manual judgment.

Description

technical field [0001] The present invention relates to the technical field of data processing, and more specifically relates to a method and system for detecting outlier assets based on machine learning. Background technique [0002] With the deepening of the integration of e-government and informatization, the network and business systems have become the infrastructure of various organizations for daily office work, while the servers carrying the business systems, the terminals for daily office work, and the security equipment for ensuring the internal information security of the organization, As the tangible assets of the organization, their long-term stable operation is the basic guarantee for the smooth development of business. [0003] Although traditional monitoring software does play a certain role in discovering equipment failures and abnormal operation, the existing monitoring software can only detect equipment assets that are faulty and abnormally operating, and c...

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): G06F16/22G06F16/2458G06K9/62G06N20/00
CPCG06F16/2282G06F16/2462G06N20/00G06F18/23213
Inventor 韩旭东曲志峰张胜猛高峰
Owner 中孚安全技术有限公司
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