Dynamic Authentication System Using Machine Learning
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Solution Overview
Problem
Conventional authentication methods rely on static data, which provides minimal security as repeated use of pre-stored challenge questions and answers increases the risk of unauthorized access, offering limited protection against unauthorized activities.
Innovation Solution
A system that uses machine learning to dynamically generate authentication questions and responses in real-time, incorporating data from various sources, including user location and transaction history, and employs unique codes and temporarily stored biometric data for enhanced security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static authentication data (pre-stored questions and answers) is used, then the authentication process is simple and fast, but security is weak because repeated use increases opportunities for unauthorized access
Solution Approach 1:
The patent applies dynamics by transitioning from static pre-stored authentication questions to dynamic questions generated in real-time. The system generates new authentication questions based on user data, event context, and machine learning models, ensuring that authentication data changes with each use rather than remaining fixed, thereby improving security while managing complexity through automated generation processes
Solution Approach 2:
The patent changes parameters by using machine learning models to dynamically adjust authentication question parameters based on multiple factors including user behavior patterns, event risk levels, and data sensitivity. This allows the system to adapt authentication difficulty and type according to specific contexts, improving security without requiring a completely complex system architecture
2Reliability
If dynamically generated authentication data is used, then security is enhanced, but the authentication process becomes more complex and requires more processing time
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing user profiles, behavior patterns, and contextual data before authentication events occur. The machine learning models are trained in advance on user data, and the system maintains pre-processed user information that can be quickly retrieved during authentication, reducing real-time processing requirements while maintaining dynamic security
Solution Approach 2:
The system applies self-service by using machine learning models that automatically generate authentication questions and evaluate responses without requiring manual intervention. The system self-adjusts authentication parameters based on analyzed user behavior and event context, reducing processing time by eliminating manual configuration and decision-making steps
3Object-affected harmful factors
If dynamically generated authentication questions are used, then unauthorized access is reduced, but the system requires access to multiple data sources and machine learning analysis
Solution Approach 1:
The patent applies universality by designing a multi-functional system where the same machine learning infrastructure serves multiple purposes: generating authentication questions, analyzing user behavior patterns, contextualizing events, and evaluating authentication responses. This consolidates what could be separate complex systems into a unified platform, reducing overall system complexity while maintaining strong protection against unauthorized access
Data Source
AI summary
Systems for dynamic authentication are provided. In some examples, a system may receive a request to process an event. In some examples, the request to process the event may include additional details associated with the event. The system may initiate dynamic authentication functions and may retrieve data from a plurality of sources. In some examples, the data from the plurality of sources may be analyzed using machine learning to dynamically generate authentication data, such as one or more authentication questions. The system may also generate one or more corresponding responses or answers to the one or more authentication questions. In some examples, the one or more authentication questions may be transmitted to a user device or other device and may be displayed to the user. The user may provide authentication response data that may be analyzed by the system to determine whether it matches the generated response or answer. If so, the user may be authenticated and/or the event may be processed.