User Authentication via Behavioral Data Matching
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Solution Overview
Problem
User authentication in digital communications faces challenges, particularly in remote verification over networks, where traditional methods are vulnerable to man-in-the-middle attacks and require additional security measures to ensure identity validation.
Innovation Solution
A system that authenticates user identities by utilizing data rows from trusted third-party systems, including aggregator and monitoring systems, to match user actions over time, providing a confidence score for authentication, thus enhancing security by requiring malicious actors to replicate extensive user activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional authentication methods (ID and password) are used, then ease of operation is improved, but reliability is worsened due to vulnerability to man-in-the-middle attacks
Solution Approach 1:
The patent introduces trusted third-party systems (aggregator systems and monitoring systems) as intermediaries between the user and the authentication system. These intermediaries collect and verify behavioral data from multiple sources, creating a layered verification process that prevents direct interception attacks while maintaining user convenience.
Solution Approach 2:
The system performs preliminary data collection and analysis by gathering behavioral data from trusted third-party systems before the actual authentication moment. This pre-collection of behavioral patterns, transaction histories, and device information enables the system to establish a baseline of normal user behavior that can be compared during authentication.
2Reliability
If additional authentication factors are added to improve security, then reliability is improved, but device complexity is worsened
Solution Approach 1:
The system automatically collects behavioral data from multiple trusted third-party systems without requiring user intervention. The aggregator system autonomously gathers information from monitoring systems, analyzes behavioral patterns, and generates authentication verification, eliminating the need for users to manually provide multiple authentication factors.
Solution Approach 2:
The authentication system is designed to work with multiple different trusted third-party systems simultaneously, using a unified approach to collect and analyze various types of behavioral data. This multi-functional capability allows the system to leverage diverse data sources (transactions, communications, device usage) through a single authentication framework.
3Reliability
If behavioral data from multiple trusted systems is collected and analyzed, then reliability is improved through better identity verification, but loss of time is worsened due to data matching requirements
Solution Approach 1:
Behavioral data from trusted third-party systems is collected and pre-processed before authentication is needed. The system maintains up-to-date profiles of user behavioral patterns, transaction histories, and device information, so that during authentication, the system only needs to perform matching operations rather than collecting raw data from multiple sources in real-time.
Data Source
AI summary
A method for authenticating a user identity linked to a user account may include receiving information that asserts a user identity including a user identifier, accessing external data stores to receive data rows that are associated with the user identity, and accessing monitoring systems to receive data vectors. The monitoring systems may monitor transmissions to receiving systems, the data vectors may include numerical target values for the receiving systems, and the data vectors may be accessed using the user identifier. The method may also include determining whether the data rows can be matched to the data vectors, and based on that determination, authenticating the user identity.