Authentication System Using Machine Learning for User Error Detection
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Solution Overview
Problem
Existing authentication systems face challenges in accurately distinguishing between genuine user input errors and malicious attempts, leading to increased user frustration and potential cybersecurity vulnerabilities.
Innovation Solution
The implementation of a system that utilizes machine learning to monitor authentication input and modify authentication requirements based on the detection of user-specific input errors, allowing for temporary adjustments to authentication processes to accommodate common user mistakes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Strength
If long and complicated passwords are required to strengthen security against systematic attacks, then security strength is improved, but user input ease deteriorates
Solution Approach 1:
The system pre-generates hashes for common typing errors (e.g., transpositions, omissions, insertions) and stores them in a lookup table before authentication occurs. When a user enters a password, the system checks not only the exact password but also against these pre-computed error variants, allowing users to authenticate even with minor typing mistakes while maintaining security
Solution Approach 2:
The system anticipates potential user input errors by creating a buffer of acceptable variations around the correct password. This cushioning layer includes hashes of common typo patterns, so that when users make minor mistakes, they still fall within the acceptable authentication range, preventing authentication failures due to human error
2Reliability
If multi-factor authentication is implemented to protect against phishing and social engineering attacks, then security reliability is improved, but user operation complexity increases
Solution Approach 1:
The system implements selective MFA where multi-factor authentication is not universally required but applied partially based on risk assessment. When the fuzzy matching system detects a high-confidence match between entered password and stored password (accounting for typical user typing patterns), the system may waive the second factor requirement, thus reducing operational complexity while maintaining reliability through conditional application
3Measurement precision
If the system strictly validates exact password matches to prevent security breaches, then security precision is improved, but user experience deteriorates due to false rejection of valid users
Solution Approach 1:
The system applies different validation strictness at different levels: exact matching is required for the password hash comparison, but fuzzy matching is applied to the plaintext input comparison. This local differentiation allows the system to maintain cryptographic security precision while accommodating human typing variations in the user interface layer
Solution Approach 2:
The system changes the matching parameter from exact equality to fuzzy similarity based on edit distance metrics. By adjusting the tolerance parameter to allow for a certain number of character differences (insertions, deletions, substitutions, transpositions), the system transforms the rigid exact-match validation into a flexible similarity-based validation that maintains security while improving user experience
Data Source
AI summary
In some embodiments, a computing system may monitor authentication input and modify authentication requirements based on detection of user-specific input errors. The computing system may use machine learning or other techniques to detect whether an incorrect authentication input corresponds to a common input mistake of a user. If the incorrect authentication input does correspond to a common input mistake of the user, a computing system may modify one or more authentication requirements to make the authentication process easier for the user.