Machine Learning User Authentication with Immutable Audit Trails
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Solution Overview
Problem
Existing user authentication methods are prone to abuse and fail to provide a safe, secure, timely, and immutably auditable authentication process, vulnerable to data breaches, stolen devices, SIM swapping, and deep fakes.
Innovation Solution
A method and system utilizing a processing device to initiate an authentication session, identify authentication prompts, transmit them to user devices, receive responses, analyze using machine learning models, generate an authentication status, and store it, incorporating point-in-time authentication on a blockchain for immutable record-keeping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional authentication methods are used, then the authentication process is simple and fast, but the system is vulnerable to fraud, data breaches, and security attacks
Solution Approach 1:
The authentication system is segmented into multiple independent verification factors (biometric data, device identifiers, location data, behavioral patterns) that work together to provide comprehensive security. Each factor is processed separately through machine learning models, allowing the system to maintain high security while managing complexity through modular architecture.
Solution Approach 2:
Machine learning models serve as intermediaries between raw authentication data and security decisions. These models analyze multiple data sources (biometric scans, device metadata, user behavior) and translate them into reliable authentication outcomes, reducing system complexity by centralizing the decision-making process in intelligent intermediaries.
2Reliability
If multi-factor authentication is implemented, then authentication security is improved, but user experience may deteriorate due to additional verification steps
Solution Approach 1:
The system performs self-service by automatically collecting and analyzing multiple authentication factors without requiring active user participation in each verification step. Biometric sensors, device identifiers, and location services automatically provide data to the machine learning models, which then make authentication decisions without user intervention beyond the initial biometric scan.
Solution Approach 2:
The system performs preliminary actions by continuously monitoring and analyzing user behavior patterns, device characteristics, and authentication attempts before critical security decisions are required. This pre-analysis allows the system to make faster, more accurate authentication decisions in real-time, improving both security and user convenience.
3Reliability
If authentication data is stored traditionally, then access and processing is fast, but the data may be compromised in data breaches and lacks immutable audit trails
Solution Approach 1:
Authentication data is segmented into critical immutable elements (authentication outcomes, timestamps, user identifiers) stored on blockchain for integrity, and non-critical operational data (biometric templates, device details) stored in traditional databases for fast access. This segmentation allows the system to maintain both data integrity and processing speed by placing different data types in appropriate storage environments.
4Loss of information
If point-in-time authentication is implemented on blockchain, then immutable audit trails are provided, but system complexity and processing time increase
Solution Approach 1:
The system extracts only the essential authentication elements (authentication outcome, timestamp, user identifier, transaction hash) and stores them on the blockchain, while keeping the complex authentication logic, biometric data, and device information in traditional systems. This extraction approach provides immutable audit trails without requiring the entire authentication system to be built on blockchain, thereby reducing overall system complexity.
Data Source
AI summary
The present disclosure provides a method of facilitating authenticating of users. Further, the method includes initiating, using a processing device, an authentication session for a user for an authentication instance. Further, the method includes identifying, using the processing device, authentication prompts for the authenticating of the user based on the initiating. Further, the method includes transmitting, using a communication device, the authentication prompts to user devices. Further, the method includes receiving, using the communication device, data in response to the authentication prompts from the user devices. Further, the method includes analyzing, using the processing device, the data using machine learning models. Further, the method includes generating, using the processing device, an authentication status for the user based on the analyzing. Further, the method includes terminating, using the processing device, the authentication session based on the generating. Further, the method includes storing, using a storage device, the authentication status.