Identity authentication system and method based on user habit behavior features

An identity authentication and behavior technology, applied in the field of identity authentication systems, can solve the problems of capturing subtle changes in user habits and behavior, and the inability to identify the user himself in behavior changes, so as to reduce the risk of passing authentication and improve security.

Active Publication Date: 2016-10-12
SHENZHEN ALL IN ONE NEW TECH LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

The technical effect of this patented technology improves security without increasing their size but still allowing them to recognize more activities than just finger movements alone. It can identify individuals who are likely going abusing resources such as credit card numbers during transactions while avoid being identified with impostors' fingers.

Problems solved by technology

This patented technical problem addressed in the patents relates to improving securely identifying individuals through various means like password-base identification technologies while maintaining ease of usage and usherence from being lost or stolen. Additionally, current recognition techniques require expensive equipment attached to each device or process module, making them difficult to integrate onto other intelligently connected devices without compromising its functionality.

Method used

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  • Identity authentication system and method based on user habit behavior features
  • Identity authentication system and method based on user habit behavior features
  • Identity authentication system and method based on user habit behavior features

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0108] The specific implementation process of this method in the offline micropayment application is as follows:

[0109] S1, starting from the user installing the offline micropayment application, the following raw behavioral data samples are continuously collected: the merchant, time point, single consumption amount of the user each time, and gestures when approaching the payment terminal, recorded as: u( x, y, z, t), where x represents the single consumption amount, y represents the merchant, t represents the consumption time, and z represents the gesture feature value;

[0110] S2, continuous collection for one week to obtain 20 effective samples, U={u 1 ,u 2 ,...,u 20}

[0111] S21, calculate the standard deviation of the four dimensions of single consumption amount x, merchant y, consumption time t, and gesture action characteristic value z, respectively recorded as Set the variance threshold to be There are the following discrimination results:

[0112]

[01...

Embodiment 2

[0131] S1 extracts all sensing parameters on the user's mobile terminal;

[0132] S2 collects business data that completes specified business actions;

[0133] S3 saves the collected business data into the corresponding type of original behavior data sample set;

[0134] S4 judges whether habitual behavior characteristics have been found out;

[0135] If the habitual behavior feature is found, it is judged whether the value range of the user's habitual behavior degree is within the range of the habitual behavior feature;

[0136] If it is within the range, the authorization is successfully authenticated and the authorization level is increased at the same time;

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Abstract

The invention relates to an identity authentication system and method based on user habit behavior features. The identity authentication system comprises a configuration module, a collecting muddle, a sample processing module, a sample quantity judging module, a variance calculation module, a combined dimension module, a habit behavior dimension composition module and an authentication module, wherein besides dimensions such as gestures and shaking, dimensions such as activity regions, acting tracks, touch force and trading behaviors are also collected by the collecting module; then, a plurality of behavior habits are extracted through data mining for identity recognition. The problems that in the prior art, the user behavior features are collected only through once collection at the initial stage, and the generated slight change of the user habit behaviors cannot be caught along with the timelapse, so that the user per se cannot be recognized when the behavior change exceeds the critical value are solved. The risk that the gesture action is imitated or an unauthenticated user passes through the authentication due to sampling is further reduced.

Description

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Claims

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Application Information

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Owner SHENZHEN ALL IN ONE NEW TECH LTD
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