Two-level sequence learning for password strength evaluation
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Solution Overview
Problem
Current password management systems lack a standardized approach to measuring password strength, leading to vulnerabilities due to the use of easily guessable passwords, which can result in identity theft and data loss, as users often choose common passwords to facilitate memory rather than security.
Innovation Solution
A computer-implemented method using two-level sequence learning to analyze, meter, generate, and crack passwords by categorizing existing passwords into semantic categories, learning composition patterns, and evaluating candidate passwords based on defined strength thresholds, thereby enhancing password security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users choose common passwords to facilitate memory, then ease of operation is improved, but password strength deteriorates
Solution Approach 1:
The system performs preliminary analysis of password patterns and entropy calculations before the user finalizes their password choice. By pre-evaluating candidate passwords against learned composition patterns and semantic categories, the system can warn users about weak passwords before they are set, allowing users to choose memorable passwords while avoiding those with low entropy.
2Measurement precision
If systems provide password strength meters with different rules, then measurement capability is improved, but standardization deteriorates
Solution Approach 1:
The machine learning component is trained on a diverse dataset of existing passwords and learns universal composition patterns and semantic categories that apply across different password contexts. This enables the system to provide standardized password strength evaluation that can be consistently applied across multiple systems and platforms, while still adapting to different password policies and requirements.
3Measurement precision
If a machine learning component learns composition patterns from existing passwords, then password analysis capability is improved, but processing complexity increases
Solution Approach 1:
The password analysis process is segmented into distinct stages: training phase where the machine learning component learns from existing passwords, and evaluation phase where candidate passwords are assessed. During training, the system learns composition patterns and semantic categories separately, then combines this knowledge for evaluation. This segmentation allows complex pattern recognition to be broken down into manageable components that can be processed efficiently.
Data Source
AI summary
Managing passwords is provided. A machine training process is performed using a set of existing passwords to train a machine learning component. Members of a set of semantic categories are used to categorize respective passwords in the set of existing passwords. Password strengths corresponding to a set of candidate passwords are evaluated using the machine learning component. A resource is secured with a candidate password having a password strength greater than or equal to a defined password strength threshold level.