Adaptive Authentication for Electronic Signatures via ML Anomaly Scoring
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Solution Overview
Problem
Existing user authentication processes in electronic signature workflows are inadequate, particularly in preventing spoofing attacks and ensuring dynamic augmentation of authentication levels based on risk assessment.
Innovation Solution
The implementation of advanced machine learning classification models that evaluate parameters from computing hardware and software involved in document signing requests, generating an anomaly score to determine additional authentication requirements dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simple username and password validation is used, then ease of operation is improved, but reliability is worsened due to susceptibility to spoofing attacks
Solution Approach 1:
The system dynamically changes authentication parameters by generating activity variables from multiple data sources (device parameters, behavioral patterns, transaction context) and using machine learning models to determine risk-based authentication requirements, transforming static credential validation into adaptive multi-parameter assessment
Solution Approach 2:
The machine learning classification model acts as an intermediary between the authentication request and the authentication decision, analyzing activity variables and generating anomaly scores that determine whether additional authentication factors are required, mediating between simplicity and security needs
2Reliability
If advanced machine learning authentication analysis is implemented, then reliability is improved, but device complexity is worsened
Solution Approach 1:
The authentication system is segmented into distinct functional modules: activity variable generation from multiple sources, machine learning classification model processing, anomaly score generation, and dynamic authentication requirement determination, allowing complex functionality to be managed through modular components
Solution Approach 2:
The machine learning model automatically analyzes authentication requests and self-determines the appropriate authentication level required, eliminating the need for manual security policy configuration and reducing operational complexity despite increased system intelligence
3Adaptability or versatility
If dynamic authentication requirements are implemented, then adaptability is improved, but productivity is worsened due to additional authentication steps
Solution Approach 1:
The system applies partial authentication actions by dynamically determining the appropriate level of authentication based on risk assessment, applying only the necessary authentication factors (sometimes just username/password, sometimes additional factors) rather than always requiring maximum authentication, thus balancing security with transaction efficiency
Solution Approach 2:
The authentication requirements dynamically adjust based on real-time analysis of activity variables and anomaly scores, allowing the system to flexibly modify authentication depth during transactions rather than using fixed authentication levels, enabling adaptability without constant additional steps
Data Source
AI summary
Methods and apparatuses are described for user authentication during an electronic signature workflow. A server authenticates user credentials included in an electronic signature request. The server generates activity variables based upon parameters associated with the electronic signature request. The server creates a multidimensional vector using the activity variables. The server executes a trained machine learning classification model on the multidimensional vector to generate an anomaly score for the electronic signature request. The server determines additional authentication requirements for the electronic signature request based upon the anomaly score. The server initiates the additional authentication requirements for the electronic signature request, including validating user authentication data received from remote computing devices.