Touchscreen Keypress Behavior Pattern Analysis for Identity Recognition
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Solution Overview
Problem
Traditional electronic commerce authentication methods, such as usernames and passwords, are ineffective in securing user accounts on mobile terminals, as stolen credentials can be easily mimicked or compromised.
Innovation Solution
A touch screen user keypress behavior pattern construction and analysis system using RBF neural networks to establish unique keyboard behavior models based on time and pressure characteristics, allowing for authentication by matching user input patterns on mobile devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional username and password authentication is used, then the authentication process is simple and easy to operate, but the security of user accounts cannot be effectively guaranteed when credentials are stolen
Solution Approach 1:
The patent introduces an intermediary behavioral pattern recognition system between the user and the authentication process. Instead of directly relying on passwords, the system mediates authentication through analyzing typing behavior patterns (key press timing, duration, rhythm) as an intermediate layer that verifies user identity without adding complex user actions
Solution Approach 2:
The patent replaces the mechanical password entry system with a behavioral analysis system that automatically captures and analyzes typing dynamics. The mechanical act of typing is substituted with automated sensing of typing patterns through the keyboard interface, eliminating the need for users to perform additional security actions
2Reliability
If behavioral pattern analysis is added to authentication, then security is improved, but the system complexity increases
Solution Approach 1:
The patent makes the existing keyboard interface multi-functional by enabling it to serve both its original purpose (inputting text/passwords) and the new function (capturing behavioral patterns for authentication). The same hardware infrastructure is used for dual purposes, avoiding additional complex devices
Solution Approach 2:
The system performs self-service by automatically capturing typing behavior data during normal password entry without requiring users to consciously provide additional information. The behavioral patterns are collected passively as users naturally type, eliminating the need for separate authentication actions
3Measurement precision
If multiple authentication factors are implemented, then detection accuracy improves, but user operation burden increases
Solution Approach 1:
The patent merges the authentication process with the password entry process itself. The behavioral analysis is combined with the existing password input action, so that a single operation (typing the password) simultaneously serves both authentication purposes without requiring separate steps
Data Source
AI summary
A construction and analysis system of touch screen user keypress behavior pattern, and an identity recognition method thereof. Data analysis is performed by using historical keypress information of inputting a password by using a soft keyboard, a corresponding neural network model is established and model calculation is performed to new to-be-detected data to recognize a user identity; the system consists of a user data acquisition module, a data preprocessing module, a model training module and a user identity authentication module; the user data acquisition module is responsible for acquiring time sequence information, pressure and contact area information; the data preprocessing module is responsible for acquiring data, removing dirty data and normalizing to the data; the model training module is responsible for analysing input patterns and establishing models; and the user identity authentication module is for performing model calculation to new to-be-detected data to recognize user identities and improve the security of user account passwords.