Abnormal user detection method and device based on clustering analysis, equipment and medium

A technology of cluster analysis and detection method, applied in the field of artificial intelligence, can solve the problem of low recognition accuracy, and achieve the effect of improving recognition efficiency, avoiding inaccurate recognition, and improving efficiency

Pending Publication Date: 2020-10-16
CHINA PING AN PROPERTY INSURANCE CO LTD
View PDF0 Cites 12 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Embodiments of the present invention provide a method, device, device, and medium for detecting abnormal users based on cluster analysis, so as to solve the problem of low recognition accuracy of traditional methods for identifying abnormal users

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
  • Abnormal user detection method and device based on clustering analysis, equipment and medium
  • Abnormal user detection method and device based on clustering analysis, equipment and medium
  • Abnormal user detection method and device based on clustering analysis, equipment and medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0031] The abnormal user detection method based on cluster analysis provided by this application is applied to the server, and the server can be implemented by an independent server or a server cluster composed of multiple servers. In one embodiment, as figure 1 As shown, a method for detecting abnormal users based on cluster analysis is provided, including the following steps:

[0032] S1: Obtain the basic data corresponding to each user from the pre...

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 relates to artificial intelligence, and provides an abnormal user detection method and device based on clustering analysis, equipment and a medium. The abnormal user detection method based on clustering analysis comprises the steps of obtaining basic data corresponding to each user from a preset database; performing feature extraction on the basic data according to a preset requirement to obtain feature data; performing one-hot encoding on the feature data to generate user portrait behavior data corresponding to each user; performing clustering analysis processing by utilizing all the user portrait behavior data to obtain a normal clustering group; calculating the matching degree between each piece of user portrait behavior data and the normal clustering group, and judging whether the matching degree is abnormal or not; and determining the user corresponding to the user portrait behavior data with the abnormal matching degree as an abnormal user. The invention further relates to a blockchain technology, the basic data can be stored in the blockchain, and the technical scheme of the invention realizes accurate identification of abnormal users and identification efficiency.

Description

technical field [0001] The present invention relates to artificial intelligence, in particular to a method, device, equipment and medium for abnormal user detection based on cluster analysis. Background technique [0002] With the development of network technology, Internet financial risks are more concealed and easy to expand than the rule-based strategies used by traditional finance. Due to the phenomenon of fraud by abnormal users, the welfare of ordinary consumers is robbed, and even losses are caused to enterprises. In order to To protect the legitimate rights and interests of enterprises and ordinary consumers, it is necessary to identify abnormal users. The traditional method of identifying abnormal users is mainly based on rules and login verification risk control methods, but there is a problem of low identification accuracy. Contents of the invention [0003] Embodiments of the present invention provide a method, device, device, and medium for detecting abnormal ...

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): G06K9/62G06Q10/06G06Q40/00
CPCG06Q10/0635G06Q40/00G06F18/23213G06F18/22
Inventor 敖琦唐炳武
Owner CHINA PING AN PROPERTY INSURANCE CO LTD
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap