ML-Based Auto-Fill for Secure Form Input Classification
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Solution Overview
Problem
Current password managers are inefficient in accurately auto-filling input information across different network services, often inaccurately filling data and failing to determine the correct type of information required, which leads to resource wastage and security risks.
Innovation Solution
A cyber security service provider's infrastructure analyzes source codes to determine characteristics of form fields, calculates training signatures, trains a machine learning model, and transmits it to user devices to accurately identify and auto-fill input information in observed fields by correlating observed characteristics with known types of input information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional password managers are used for auto-filling information, then the auto-fill function is provided, but the accuracy of determining the correct type of information is low and security risks increase
Solution Approach 1:
The patent replaces traditional rule-based or keyword-matching mechanisms with a machine learning model that uses trained signatures to classify field portions. The ML model analyzes characteristics of field portions and determines the type of input information required with higher accuracy, thereby improving both measurement precision and reliability without requiring manual configuration or complex security rules.
2Productivity
If traditional auto-fill methods are used, then the filling process is simple, but resource wastage occurs due to inaccurate data filling
Solution Approach 1:
The system implements feedback by continuously analyzing the results of auto-fill operations and using this information to refine the machine learning model's classification accuracy. The ML model learns from observed characteristics and improves its ability to correctly identify field portion types, reducing resource wastage from incorrect fills while maintaining high productivity through automated operation.
3Reliability
If complex security measures are implemented to protect sensitive data, then security is improved, but the complexity of the system increases
Solution Approach 1:
The machine learning model operates autonomously on the user device, performing local classification of field portions without requiring constant communication with remote servers or complex centralized security infrastructure. This self-service approach enhances security by keeping sensitive data processing local while minimizing system complexity through the use of a standalone ML model that requires no additional security layers.
Data Source
AI summary
A system is disclosed in which an infrastructure device analyzes a source code associated with a known network element to determine a characteristic associated with a field portion that is configured to accept a known type of input information; determines a correlation between the characteristic and the known type of input information; calculates a training signature to classify the field portion; trains an ML model based on the training signature; and transmits a trained ML model to a user device. The user device analyzes an observed source code associated with an observed network element to determine an observed characteristic associated with an observed field portion that is configured to accept a given type of input information; calculates an observed signature; utilizes the trained ML model to determine the given type of input information; and auto-fills, in the observed field portion, input information according to the given type of input information.