US 2021/0240822 A1 · Abnormal User Identification

Abstract

User behavior data of multiple users is acquired, and multiple user eigenvalues of user behavior data of each user under preset multiple user behavior dimensions are extracted. A user eigenvector of each user is determined based on the multiple eigenvalues of this user. Multiple user classes are obtained by clustering the user eigenvectors of multiple users are clustered through a preset clustering algorithm. A central vector of each user class is determined based on the user eigenvectors included in this user class. A difference eigenvector of each user class is determined, wherein a distance between the difference eigenvector and a central vector of an aggregation class to which the difference eigenvector belongs is not within a preset distance range. A user characterized by the difference eigenvector is determined as an abnormal user.

Claims17

Dependent claims

An abnormal user identification method, comprising:

acquiring user behavior data of a user;
extracting multiple eigenvalues of the user behavior data under preset multiple behavior dimensions;
determining eigenvectors for the user behavior data based on the multiple eigenvalues;
obtaining multiple aggregation classes by clustering the eigenvectors through a preset clustering algorithm, and acquiring a central vector of each aggregation class;
determining a difference eigenvector, wherein a distance between the difference eigenvector and a central vector of an aggregation class to which the difference eigenvector belongs is not within a preset distance range;
and
determining a user characterized by the difference eigenvector as an abnormal user.

4 more elements

An electronic device, comprising a processor and a machine readable storage medium which stores machine executable instructions that can be executed by the processor, wherein, the machine executable instructions cause the processor to:

acquire user behavior data of a user;
extract multiple eigenvalues of the user behavior data under preset multiple behavior dimensions;
determine eigenvectors for the user behavior data based on the multiple eigenvalues;
obtain multiple aggregation classes by clustering the eigenvectors through a preset clustering algorithm, and acquire a central vector of each aggregation class;
determine a difference eigenvector, wherein a distance between the difference eigenvector and a central vector of an aggregation class to which the difference eigenvector belongs is not within a preset distance range;
and
determine a user characterized by the difference eigenvector as an abnormal user.

4 more elements

Dependencies are inferred from the claim text.

Abnormal User Identification

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Abnormal User Identification

Life of this patent

Abstract

Claims17