Identity recognition method, device and equipment based on keystroke characteristics of mobile equipment and medium

A mobile device and identity recognition technology, applied in the field of identity recognition, can solve the problems of decreased accuracy and inability to perfectly match the keystroke characteristics of mobile users, and achieve the effects of improving accuracy, reducing the amount of input data, and easy expansion.

Active Publication Date: 2021-06-25
CENT SOUTH UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Therefore, when the keystroke feature recognition model based on an ordinary physical keyboard is migrated to the mobile terminal, the existing model is often not able to perfectly match the keystroke features of the mobile terminal user, resulting in a decrease in accuracy

Method used

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  • Identity recognition method, device and equipment based on keystroke characteristics of mobile equipment and medium
  • Identity recognition method, device and equipment based on keystroke characteristics of mobile equipment and medium
  • Identity recognition method, device and equipment based on keystroke characteristics of mobile equipment and medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0061] This embodiment provides an identification method based on keystroke features of a mobile device, including:

[0062] Step 1. For the user whose identity is to be identified, extract the keystroke features from the keystroke process of the user logging in to the mobile device: the leap time series, the residence time series and the contact coordinate sequence, and calculate the relationship between each keystroke feature and the keystroke during registration. Variance, Euler distance and Pearson correlation coefficient between key features to obtain the following 9 characteristic parameters of user login: leap time series variance, leap time series Euler distance, leap time series Pearson correlation coefficient, stay time series variance , Euler distance of residence time series, Pearson correlation coefficient of residence time series, variance of contact coordinate sequence, Euler distance of contact coordinate sequence and Pearson correlation coefficient of contact c...

Embodiment 2

[0120] This embodiment provides an identification device based on keystroke features of mobile equipment, such as image 3 As shown, it includes: a keystroke feature collection module, a comparison module, an identity recognition model, a database module and a feature update module; where:

[0121] The keystroke feature collection module is used for: extracting keystroke features from the keystroke process of logging in the mobile device for the user whose identity is to be identified: leap time sequence, residence time sequence and contact coordinate sequence;

[0122] The comparison module is used to: calculate the variance, Euler distance and Pearson correlation coefficient between each keystroke feature and the keystroke feature at the time of registration, and obtain the following 9 feature parameters of user login: leap time series variance, leap Time Series Euler Distance, Leap Time Series Pearson Correlation Coefficient, Stay Time Series Variance, Stay Time Series Eule...

Embodiment 3

[0127] Embodiment 3 provides an electronic device, including a memory and a processor, and a computer program is stored in the memory. It is characterized in that, when the computer program is executed by the processor, the processor implements the embodiment 1. the method described.

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Abstract

The invention discloses an identity recognition method, device and equipment based on keystroke characteristics of mobile equipment and a medium, and the method comprises the steps: extracting the keystroke characteristics of a user whose identity is to be recognized from the keystroke process of logging in the mobile equipment: a leap time sequence, a residence time sequence and a contact coordinate sequence; calculating the variance, Euler distance and Pearson's correlation coefficient between each keystroke feature and the keystroke feature during registration, and obtaining nine feature parameters of user login; inputting the obtained nine characteristic parameters into a pre-trained identity recognition model, and judging whether the identity of the current login user is legal or not according to model output, wherein the identity recognition model is obtained by adopting a feedforward neural network and training based on a plurality of positive and negative samples, and the positive and negative samples are respectively composed of nine characteristic parameters obtained when legal and illegal users log in.

Description

technical field [0001] The invention belongs to the technical field of identification, and in particular relates to an identification method, device, equipment and medium based on keystroke characteristics of a mobile device. Background technique [0002] Currently, in computer systems / platforms that require users to perform login verification, user accounts and passwords are often the most common verification methods. However, this verification method cannot avoid the risk of account theft. Therefore, many systems / platforms have adopted SMS verification, email verification, fingerprint / face recognition and other methods to confirm user identity, but the above methods often have limitations such as information leakage and unsupported equipment. Therefore, some systems / platforms combine keystroke feature recognition modules to ensure user information security. [0003] The keystroke feature in the traditional sense refers to the rhythm of the user typing on the keyboard whe...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F21/31G06K9/62G06N3/08G06F3/0488
CPCG06F21/316G06N3/08G06F3/0488G06F18/22
Inventor 朱承璋肖亚龙黄奕鑫杨翔王晗
Owner CENT SOUTH UNIV
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