Machine Learning Password Generation for Strength and Memorability
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Solution Overview
Problem
Current password management solutions fail to generate passwords that are both strong and memorable, often requiring storage in password wallets or written solutions that are susceptible to breaches, and users are compelled to remember complex passwords despite the risk.
Innovation Solution
A machine learning model, such as a generative pre-trained transformer (GPT), automates the generation of strong and memorable passwords by identifying user-specific information, eliminating OSINT-derived words, and combining memorable portions to create unique, non-dictionary passwords, while ensuring they are not easily guessable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Strength
If passwords are made strong using complex characters and patterns, then security strength is improved, but memorability deteriorates
Solution Approach 1:
The password is segmented into multiple components: a base word derived from user-specific information, suffixes, prefixes, and optional character substitutions. This segmentation allows each component to serve a specific function - the base word provides memorability while suffixes/prefixes enhance strength, resolving the contradiction between strong and memorable passwords
Solution Approach 2:
The system introduces an intermediary machine learning model that acts as a mediator between user information and password generation. The model processes personal information to generate memorable base words while incorporating security requirements, bridging the gap between memorability and strength requirements
2Ease of operation
If common words and personal information are used to make passwords memorable, then memorability is improved, but security vulnerability increases due to OSINT attacks
Solution Approach 1:
The system extracts and removes vulnerable elements from password generation by filtering out commonly used words and high-risk personal information through the machine learning model. This extraction process eliminates OSINT vulnerabilities while preserving the ability to generate memorable passwords from safe user information
Solution Approach 2:
The system converts potentially harmful personal information into beneficial password components by using the machine learning model to process user data in a way that creates memorable but secure base words. The model transforms risky information into safe, memorable elements that enhance security rather than vulnerability
3Adaptability or versatility
If password generation requires storage of personal information in ROM or network storage, then password customization is improved, but security risk increases from data breaches
Solution Approach 1:
The system implements a discard-and-recover mechanism where personal information is temporarily used during password generation, then discarded from storage. The machine learning model processes the information in memory without persistent storage, allowing customization while eliminating data breach risks associated with storing sensitive information
Solution Approach 2:
The system performs self-service by generating passwords directly from user-provided information through the machine learning model without requiring external storage infrastructure. The entire process occurs in-memory, eliminating the need for ROM or network storage of personal information while maintaining customization capabilities
Data Source
AI summary
Systems and methods for automating generation of a strong and memorable password. Systems may include a computer configured to receive units of information from the user, use a machine learning model to eliminate units of information found when searching open-source intelligence (OSINT) available about the user, use the machine learning model to identify portions of the non-eliminated units of information, and use the machine learning model to combine the portions into memorable password options. Systems may include the computer configure to use the machine learning model to eliminate any of the password options found when searching OSINT, present a subset of the non-eliminated password options to the user, receive the selected password from the user, use the machine learning model to generate an obfuscated reminder for the selected password, and provide the obfuscated reminder to the user.