Password Strength Evaluation Using Statistical Probability Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for evaluating password strength are resource-intensive and may rely on incomplete or outdated dictionaries, posing security risks and inefficiencies, especially on resource-constrained devices.
Innovation Solution
A natural language model and statistical algorithm, such as a Markov model using Bayesian inference, are employed to calculate the probability that a candidate password contains natural language words, reducing storage and computational requirements while providing real-time feedback on password strength.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a dictionary search method is used to evaluate password strength, then the evaluation can be performed on resource-constrained devices, but the storage space and computational resources required increase significantly
Solution Approach 1:
The patent extracts the essential information needed for password evaluation from comprehensive dictionaries, retaining only the most relevant word patterns and linguistic structures. This selective extraction maintains evaluation effectiveness while dramatically reducing the data volume stored on the device.
Solution Approach 2:
The patent transforms the evaluation approach by changing from storing complete word lists to storing statistical probability models of character sequences. This parameter transformation allows the system to evaluate password strength based on linguistic patterns rather than exact word matches, reducing storage requirements while maintaining security assessment capability.
2Ease of operation
If a dictionary search method is used to evaluate password strength, then the evaluation can be performed locally, but the computational time and processing power required increase significantly
Solution Approach 1:
The patent replaces the mechanical dictionary search process with a statistical probability calculation system. Instead of searching through word lists, the system uses pre-computed statistical models to quickly assess the likelihood of character sequences forming meaningful words, dramatically reducing processing time.
Solution Approach 2:
The patent performs preliminary computation by pre-calculating and storing statistical probability models of character sequences in the device. This preliminary action allows the actual password evaluation to be performed through simple probability lookups and calculations rather than time-consuming dictionary searches during user interaction.
3Measurement precision
If a complete dictionary is stored on the device for password evaluation, then evaluation accuracy is improved, but the device resources are consumed excessively
Solution Approach 1:
The patent extracts only the essential statistical patterns and probability information from complete dictionaries, discarding redundant word-by-word data. This selective extraction maintains the ability to accurately assess password strength while minimizing the resources required to store and process the evaluation data.
Data Source
AI summary
A sequence of characters may be evaluated to determine the presence of a natural language word. The sequence of characters may be analyzed to find a subsequence of alphabetical characters. Based on a statistical model of a natural language, a probability that the subsequence is a natural language word may be calculated. The probability may then be used to determine if the subsequence is indeed a natural language word.