Machine-Learning Password Guess Generation for Context-Aware Risk Assessment
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Solution Overview
Problem
Existing password strength meters fail to accurately assess password security due to their reliance on rule-based criteria, failing to account for rapidly evolving social and cultural influences, such as new slang or updates in media and pop culture, leading to users believing their passwords are secure when they are easily guessable.
Innovation Solution
An automated method using machine learning and natural language processing (NLP) to generate password guesses by leveraging online text sources, exploiting semantic structures and user/resource information to improve password security assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rule-based criteria are used to assess password strength, then the assessment method is simple and fast, but the accuracy of password security assessment deteriorates due to inability to account for evolving social and cultural influences
Solution Approach 1:
The patent transitions from static rule-based criteria to dynamic machine learning models that continuously adapt to evolving password patterns. The system retrains models with new data to capture emerging social and cultural influences, making the assessment dynamically responsive rather than fixed.
Solution Approach 2:
The patent replaces mechanical rule-based assessment with intelligent machine learning systems. Instead of predefined rules, the system uses trained models that automatically learn and adapt to password patterns, substituting rigid mechanical evaluation with flexible intelligent assessment.
2Measurement precision
If machine learning models are used to generate password guesses, then the password security assessment accuracy improves, but the computational resources and time required increase
Solution Approach 1:
The patent performs preliminary actions by pre-training machine learning models offline with extensive datasets. This allows the models to learn password patterns in advance, so that during actual assessment, the pre-trained models can quickly evaluate passwords without requiring extensive real-time computation.
Solution Approach 2:
The patent optimizes model parameters and architecture to balance accuracy and speed. By adjusting parameters such as model size, training data selection, and inference settings, the system achieves high assessment accuracy while controlling processing time and computational resource consumption.
3Measurement precision
If comprehensive user and domain information is collected and processed, then the password guess accuracy improves, but the system complexity and data processing requirements worsen
Solution Approach 1:
The patent segments the complex assessment task into distinct components: user information processing, domain information processing, and password evaluation. Each component is handled by specialized machine learning models trained on specific types of data, allowing independent optimization and management of each segment.
Solution Approach 2:
The patent develops universal machine learning models that can process multiple types of information (user profiles, domain characteristics, password patterns) through a unified framework. This multi-functional approach reduces overall system complexity compared to having separate specialized systems for each information type.
Data Source
AI summary
In an example embodiment, an efficient, automated method to generate password guesses is provided by leveraging online text sources along with natural language processing techniques. Specifically, semantic structures in passwords are exploited to aid system in generating better guesses. This not only helps cover instances where traditional password meters would indicate a password is safe when it is not, but also makes the solution robust against fast-evolving domains such as new slang in natural languages or new vocabulary arising from new products, product updates, and services.