Machine-Learned Behavioral Profiles for Authentication
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Solution Overview
Problem
Existing information systems face challenges in ensuring the safety and security of resources while optimizing technical operations, particularly in preventing unauthorized access through electronic portals, where traditional methods struggle to effectively validate user authentication requests.
Innovation Solution
The implementation of machine-learned user-account behavior profiles to process authentication requests by capturing and evaluating behavioral parameters and activity data across multiple dimensions, allowing or denying access to secured information resources based on predefined thresholds and profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional authentication methods are used to ensure security, then unauthorized access is prevented, but system efficiency and user convenience deteriorate
Solution Approach 1:
The system performs self-authentication by automatically analyzing behavioral parameters and comparing them against stored profiles without requiring manual security interventions. The computing platform autonomously determines whether to grant access based on behavioral analysis, eliminating the need for additional authentication steps while maintaining security.
Solution Approach 2:
Traditional mechanical authentication methods (passwords, tokens, biometric scans) are replaced with a computational behavioral analysis system. The system substitutes physical authentication mechanisms with machine learning algorithms that process digital behavioral data, improving both security and user convenience.
2Reliability
If traditional authentication methods are used, then security is maintained, but user convenience and ease of operation worsen
Solution Approach 1:
The authentication system operates autonomously by automatically collecting behavioral parameters, comparing them against stored profiles, and making access decisions without user intervention. Users simply perform their normal actions without needing to complete additional security steps, greatly improving convenience while maintaining security.
Solution Approach 2:
Behavioral profiles are pre-established during normal system usage by collecting and storing behavioral parameters. This preliminary action creates a baseline of expected behavior patterns that enables rapid, automated authentication decisions during actual access attempts, eliminating the need for time-consuming authentication procedures.
3Measurement precision
If behavioral parameters are captured and analyzed for each authentication request, then authentication accuracy improves, but processing time and system complexity increase
Solution Approach 1:
Behavioral profiles are pre-computed and stored during normal system operation by continuously collecting behavioral parameters. This preliminary action creates ready-to-use authentication references that enable rapid comparison during actual authentication requests, achieving high accuracy without increasing processing time.
Solution Approach 2:
The system analyzes only the specific behavioral parameters relevant to the current authentication context rather than processing all possible data. By focusing on locally relevant behavioral characteristics (device identifiers, activity patterns, timing information), the system achieves high authentication accuracy with minimal processing overhead.
4Reliability
If comprehensive behavioral data is collected for each user, then profile accuracy and security improve, but data storage requirements and system complexity increase
Solution Approach 1:
The system extracts only the essential behavioral parameters needed for authentication (device identifiers, activity patterns, timing information) rather than storing all possible user data. This selective extraction maintains profile accuracy while minimizing data storage requirements and system complexity.
Solution Approach 2:
The system stores behavioral profiles with focused granularity, capturing specific relevant characteristics rather than comprehensive universal data. Each profile contains only the local behavioral qualities necessary for that user's authentication, optimizing the balance between profile accuracy and storage efficiency.
Data Source
AI summary
Aspects of the disclosure relate to processing authentication requests to secured information systems using machine-learned user-account behavior profiles. A computing platform may receive an authentication request corresponding to a request for a user of a client computing device to access one or more secured information resources associated with a user account. The computing platform may capture one or more behavioral parameters and may authenticate the user of the client computing device to the user account based on the one or more behavioral parameters and one or more authentication credentials. The computing platform then may generate and send one or more authentication commands directing an account portal computing platform to allow access to the one or more secured information resources. Subsequently, the computing platform may capture activity data associated with one or more interactions in a client portal session and may update a behavioral profile associated with the user account.