Keystroke Dynamics Authentication via Typing Signature Analysis
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Solution Overview
Problem
The management of passwords is problematic due to the need for hard-to-guess yet hard-to-remember passwords, and existing keystroke dynamics methods require lengthy training sets for continuous authentication or rely on static methods that are not efficient for user classification.
Innovation Solution
A system that uses a keyboard interface with sensors to create keystroke objects with pressure and timing data, analyzed by a pattern recognition algorithm to generate unique typing signatures for user classification, comparing current typing habits to stored signatures for authentication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional password management is used, then users can access systems, but passwords must be hard-to-guess yet hard-to-remember creating management problems
Solution Approach 1:
The patent replaces traditional mechanical password memorization with biometric keystroke dynamics analysis. The system captures temporal and pressure characteristics of keyboard inputs to create unique typing signatures, substituting the need for users to remember complex passwords with an automated biometric authentication system that analyzes physical typing patterns.
Solution Approach 2:
The system performs self-service authentication by automatically analyzing typing patterns without requiring user intervention beyond normal keyboard input. The keystroke dynamics system continuously captures and analyzes typing characteristics, creating authentication signatures automatically as users normally type, eliminating the need for users to manually manage or remember passwords.
2Reliability
If continuous authentication with lengthy training sets is used, then user verification is improved, but system complexity and training time increase
Solution Approach 1:
The system performs preliminary action by capturing and analyzing keystroke characteristics during normal user input, building authentication signatures in advance without requiring separate training phases. The pattern recognition system continuously learns typing patterns as users normally interact with the system, eliminating the need for lengthy post-training periods.
Solution Approach 2:
The system maintains continuous authentication through ongoing analysis of keystroke dynamics during normal typing operations. Rather than requiring periodic retraining, the system continuously captures and analyzes temporal and pressure characteristics, updating authentication signatures in real-time as users interact with the system, ensuring continuous verification without interrupting useful action.
3Device complexity
If static authentication methods are used, then implementation is simpler, but user classification and identification efficiency is reduced
Solution Approach 1:
The patent applies dynamics by transitioning from static authentication to dynamic keystroke analysis. The system captures temporal variations, pressure changes, and timing characteristics of keyboard inputs to create living authentication signatures that reflect actual typing behavior patterns, enabling efficient user classification and identification through dynamic pattern recognition rather than static comparisons.
Data Source
AI summary
The present invention provides a device and method for classifying a user using pattern recognition of an input device. A series of the keystroke objects are received via the user input interface. A typing signature is determined for the series of keystroke objects using the processor by analyzing the key attributes of the series of keystroke objects using a pattern recognition algorithm. The typing signature is compared to one or more user typing signatures stored in the memory using the processor. The user is classified based on whether or not the typing signature is statistically similar to one of the stored typing signatures.