Continuous User Authentication via Machine Learning Anomaly Detection
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Solution Overview
Problem
Existing authentication methods for IT resources require frequent user intervention, leading to security work-arounds and user friction, as they often necessitate continuous authentication, which can decrease user engagement and increase the risk of personal information compromise.
Innovation Solution
A system utilizing machine learning and AI for continuous authentication based on an identification confidence score, monitoring user behavior and conduct, including biometrics, device registration, geographical location, and IP reputation, to automatically suspend or terminate processes if anomalous activity is detected, thereby reducing the need for frequent user authentication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous authentication is implemented using traditional methods (username/password), then security is improved, but user friction increases and user engagement decreases
Solution Approach 1:
The patent replaces traditional mechanical authentication methods (typing usernames and passwords) with biometric authentication (fingerprint, facial recognition, iris scanning). This substitution eliminates the need for users to manually enter credentials, thereby reducing user friction while maintaining or enhancing security through more reliable biometric verification.
Solution Approach 2:
The system performs continuous authentication automatically without requiring active user participation. Once initially authenticated, the system continuously verifies user identity in the background using biometric data from the device, eliminating the need for users to repeatedly authenticate and reducing overall user friction while maintaining continuous security monitoring.
2Ease of manufacture
If traditional authentication methods are used, then implementation is simple, but security work-arounds are discovered and exploited
Solution Approach 1:
The patent combines multiple authentication factors into a composite authentication system: biometric verification (fingerprint, face, iris), device characteristics (hardware identifiers, sensor data), behavioral patterns (typing rhythm, usage habits), and contextual information (location, time). This multi-layered composite approach creates significantly stronger security that is much harder to compromise than traditional single-factor authentication, while the system manages this complexity through automated machine learning models.
Solution Approach 2:
The system performs preliminary authentication using device characteristics and biometric data before granting access, and continuously monitors user behavior patterns in advance to detect anomalies. This proactive approach identifies potential security threats before they can be exploited, rather than reacting to breaches after they occur.
3Reliability
If frequent authentication is required, then security is strengthened, but user engagement and likelihood of continued utilization decrease
Solution Approach 1:
The system implements continuous authentication that operates in the background without interrupting user workflow. Biometric sensors continuously verify identity, and machine learning models continuously monitor behavior patterns, maintaining constant security verification while allowing users to work uninterrupted. This eliminates the need for users to stop and re-authenticate, preserving both security and user engagement.
Solution Approach 2:
The system performs authentication checks at optimized intervals based on risk assessment rather than requiring continuous user input. Low-risk activities use longer intervals between authentication checks, while high-risk actions trigger immediate verification. This periodic approach maintains security while minimizing disruption to user productivity and engagement.
4Reliability
If machine learning models are used for continuous authentication, then authentication strength is improved and user effort is reduced, but system complexity increases
Solution Approach 1:
The patent introduces machine learning models as intermediary components that automatically process and analyze raw biometric data, device characteristics, and behavioral patterns. These models serve as mediators between the complex data collection systems and the authentication decision-making process, translating raw data into meaningful authentication assessments without requiring direct user intervention or system configuration complexity.
Solution Approach 2:
The machine learning models perform multiple functions simultaneously: they authenticate user identity, detect anomalous behavior, assess security risk levels, and dynamically adjust authentication requirements. This multi-functionality consolidates what would otherwise require separate systems into a single unified platform, managing complexity while providing comprehensive security and user experience benefits.
Data Source
AI summary
Systems and methods are described herein for computer user authentication using machine learning. Authentication for a user is initiated based on an identification confidence score of the user. The identification confidence score is based on one or more characteristics of the user. Using a machine learning model for the user, user activity of the user is monitored for anomalous activity to generate first data. Based on the monitoring, differences between the first data and historical utilization data for the user determine whether the user's utilization of the one or more resources is anomalous. When the user's utilization of the one or more resource is anomalous, the user's access to the one or more resource is removed.