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Behavior analysis-oriented meta-knowledge federation method, device, electronic device and system

A behavior analysis and knowledge technology, applied in the computer field, can solve problems such as user account security cannot be guaranteed, server-side data leakage, etc.

Active Publication Date: 2020-06-02
HANGZHOU FRAUDMETRIX TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, if the behavioral data is intercepted during the upload process, or data leakage occurs on the server side, the security of the user account cannot be guaranteed, which will cause very serious consequences

Method used

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  • Behavior analysis-oriented meta-knowledge federation method, device, electronic device and system
  • Behavior analysis-oriented meta-knowledge federation method, device, electronic device and system
  • Behavior analysis-oriented meta-knowledge federation method, device, electronic device and system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0047] Embodiment 1 provides a behavior analysis-oriented meta-knowledge federation method, which aims to obtain the updated first meta-knowledge by sending the meta-knowledge update to the server through federated aggregation, and judge the updated first meta-knowledge. Whether the unary knowledge is convergent or not, the converged first meta-knowledge is used as the benchmark meta-knowledge to realize the analysis and authentication of user behavior. This method can ensure that user behavior data will not leave the client, thereby effectively protecting user data privacy, and can also ensure that each user data is used for learning to obtain more accurate meta-knowledge; the client only needs to collect a small amount of user behavior data , combined with the updated first meta-knowledge obtained by federated collection, you can quickly obtain stable custom meta-knowledge, so as to realize the analysis and authentication of user behavior on any client device, which is suitab...

Embodiment 2

[0072] Embodiment 2 is an improvement on the basis of Embodiment 1. Meta-knowledge is learned through N tasks Meta-based model training and optimization to obtain meta-knowledge update , the meta-knowledge update As the user data of the client is sent to the server, the privacy of user data on the client is effectively protected. Please refer to figure 2 As shown, meta-knowledge learning includes the following steps:

[0073] S1101. Train the meta-basic model based on the training set of a single task to obtain a single task meta-knowledge .

[0074] For a single task , using the task The training set of the meta-basic model is trained for M iterations. From time step t=0 to t=M-1, based on the preset loss function L and gradient descent algorithm, the task parameters update to , the initial value of the parameter primary knowledge . The objective function L is not limited to one of the L1 loss function, the L2 loss function, and the cross-entropy loss ...

Embodiment 3

[0090] Embodiment 3 is an improvement on the basis of Embodiment 1, the first meta-knowledge Obtained by training the basic model through the basic behavior data of the server, as the initial value of the meta-basic model trained in the meta-knowledge learning on the client.

[0091] The basic behavior data can be obtained from the user behavior database created by itself, or from the third-party user behavior database in the financial industry or other industry security verification scenarios, which is not limited here.

[0092] According to the basic behavior data, get X tasks , and assign each task Part of the behavioral data in is divided into training set and part as validation set. Specifically, in the basic behavior data, the behavior data of one user is randomly selected as label 1, and the behavior data of other users is set as label 0. In the basic behavioral data of label 1 and label 0, randomly select part of the data as the training set, and other data as th...

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Abstract

The invention discloses a meta-knowledge federation method for behavior analysis, which relates to the technical field of computers and comprises the following steps: acquiring user behavior data andreceiving first meta-knowledge; performing meta-knowledge learning based on the first meta-knowledge and the user behavior data to obtain meta-knowledge update; sending the meta-knowledge update to aserver, so that the meta-knowledge update is subjected to federated collection to obtain updated first meta-knowledge; receiving the updated first meta-knowledge and judging whether the first meta-knowledge is converged or not, if so, taking the first meta-knowledge as reference meta-knowledge, and performing behavior analysis; and if not, continuing meta-knowledge learning. According to the method, user data privacy is effectively protected, the client can quickly obtain customized meta-knowledge only through a small amount of user data, behavior analysis on any client is achieved, and the method is high in applicability, convenient to apply and good in user experience. The invention furthermore discloses a meta-knowledge federation method for behavior analysis, an electronic device, a computer storage medium and a system.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a behavior analysis-oriented meta-knowledge federation method, device, electronic equipment, storage medium and system. Background technique [0002] With the frequent incidents of data leakage and data abuse, data privacy has received more and more attention, and regulatory authorities are constantly introducing new policies to protect user data privacy. [0003] In daily life or work, there are many scenarios that involve user privacy, such as online shopping, entering passwords, browsing the web, etc. However, in financial scenarios or other security verification scenarios, more actions are involved in the actions of users entering passwords. The actions of a single user when entering their own passwords are obviously different from the behaviors of other users entering passwords. When the user enters the password, it can automatically determine whether the user is himself ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N5/02G06N3/08G06N20/00G06F21/45
CPCG06F21/45G06N3/08G06N5/022G06N20/00
Inventor 邱君华韩天奇李宏宇李晓林
Owner HANGZHOU FRAUDMETRIX TECH CO LTD
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