Machine Learning Control Engine for Dynamic User Interface Authentication
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Solution Overview
Problem
Conventional systems fail to effectively identify unauthorized users attempting to access applications, as they rely solely on login credentials for authentication, allowing unauthorized access and lacking mechanisms to prevent or mitigate unauthorized activity.
Innovation Solution
Implementing a system that uses machine learning to analyze user requests, including credentials and additional data like IP addresses and GPS locations, to determine authorized or unauthorized users, and dynamically modifying the user interface by providing decoy functionality to unauthorized users, thereby preventing access to sensitive information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional credential-based authentication is used, then authorized users can access the system, but unauthorized users can also gain access using stolen credentials
Solution Approach 1:
The system performs preliminary analysis of user requests using machine learning models before granting access. By evaluating multiple parameters (IP address, GPS location, device characteristics, request patterns) in advance, the system identifies potentially unauthorized users and presents them with decoy interfaces, preventing harmful actions before they occur.
Solution Approach 2:
The patent introduces an intermediary machine learning-based authentication system between the user and the target system. This intermediary analyzes requests, determines authorization status, and dynamically generates appropriate interfaces (authentic or decoy), acting as a mediator that blocks unauthorized access while maintaining legitimate user experience.
2Ease of operation
If the system provides functional interfaces to all users with valid credentials, then authorized users maintain access, but unauthorized users can perform harmful activities
Solution Approach 1:
The system applies different interface qualities to different users based on their authorization status. Authorized users receive fully functional interfaces with all features enabled, while unauthorized users receive decoy interfaces with disabled or fake functionality. This localized differentiation allows legitimate operations to proceed normally while blocking harmful activities.
Solution Approach 2:
The patent converts potentially harmful unauthorized access attempts into beneficial security intelligence. By presenting unauthorized users with decoy interfaces and monitoring their interactions, the system gathers data about attack patterns, credentials, and behaviors, transforming security threats into valuable information for improving future security measures.
3Reliability
If the system blocks all unauthorized access attempts, then security is improved, but legitimate users may be denied access due to false positives
Solution Approach 1:
The authentication system dynamically adjusts its behavior based on real-time analysis of user requests and machine learning model predictions. Rather than using static blocking rules, the system adapts its response (authentic interface, decoy interface, or additional verification) based on the specific characteristics of each user attempt, allowing legitimate users to access while blocking unauthorized users.
Solution Approach 2:
The system incorporates feedback loops where machine learning models continuously learn from user interactions, authentication outcomes, and security events. This feedback mechanism allows the system to improve its authorization accuracy over time, reducing false positives for legitimate users while maintaining high blocking effectiveness against unauthorized users.
4Measurement precision
If the system collects detailed user data for authentication analysis, then identification accuracy improves, but system complexity increases
Solution Approach 1:
The patent employs a universal machine learning framework that handles multiple authentication parameters (IP address, GPS location, device characteristics, request patterns, credentials) through a single integrated model. This multi-functional approach consolidates what would otherwise require separate analysis systems, reducing overall complexity while maintaining high identification accuracy.
Data Source
AI summary
Systems for detecting unauthorized user and controlling dynamic user interface functionality are provided. The system may receive a request to access functionality that may include login credentials of a user. The request may also include additional information associated with a computing device from which the request is received. The request and additional data may be analyzing using one or more machine learning datasets to determine whether a user requesting access is an authorized user or an unauthorized user. If the user is an authorized user, the user may be authenticated to the system an authentic user interface having enabled functionality may be generated. If the user is an unauthorized user, a decoy user interface having functionality disabled may be generated.