Biobehavioral Authentication via Dynamic LOA Scoring
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Solution Overview
Problem
Current authentication methods, including binary authentication and biometric verification, are insufficient in preventing unauthorized access due to vulnerabilities such as password cracking, SIM card swapping, and the misuse of biometric data, leading to potential system compromises and significant losses.
Innovation Solution
A biobehavioral derived credential system that combines human biological features and behavioral activities to provide dynamic, continuous authentication, using a cognitive engine and sensor hub to monitor user behavior and generate a unique temporal key for secure access, incorporating machine learning for anomaly detection and prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional authentication methods (passwords, biometrics) are used, then ease of operation is maintained, but security reliability deteriorates due to vulnerabilities like password cracking and SIM card swapping
Solution Approach 1:
The patent implements dynamic authentication that adapts based on risk assessment. The system continuously monitors behavioral biometrics and adjusts authentication requirements in real-time, transitioning from static password/biometric checks to adaptive multi-factor authentication when anomalies are detected. This resolves the contradiction by maintaining ease of operation during normal use while dynamically enhancing security when needed.
Solution Approach 2:
The patent combines multiple authentication factors (behavioral biometrics, device identifiers, location data, transaction patterns) into a composite authentication framework. Rather than relying on a single weak factor, the system fuses multiple data streams to create a robust security layer that maintains user convenience while significantly improving reliability through layered verification.
2Reliability
If continuous monitoring of user behavior is implemented, then security reliability improves through anomaly detection, but device complexity increases due to cognitive engines and sensor hubs
Solution Approach 1:
The patent implements self-service monitoring where the system automatically collects, analyzes, and responds to behavioral data without requiring manual intervention. The cognitive engine autonomously processes sensor data from multiple sources, detects anomalies, and triggers appropriate authentication challenges, reducing the operational complexity burden on users while maintaining high security reliability through automated surveillance.
Solution Approach 2:
The patent makes existing device components (sensors, processors, communication modules) serve multiple functions. The same sensor hub that collects data for basic device operation also gathers behavioral biometrics for security monitoring. The cognitive engine performs both standard device tasks and security analysis, reducing overall system complexity by maximizing the utility of existing hardware rather than adding dedicated monitoring components.
3Reliability
If dynamic level of assurance scoring is implemented, then security reliability improves by blocking unauthorized access, but loss of time increases due to additional authentication steps
Solution Approach 1:
The patent implements partial authentication by applying security measures proportionate to the detected risk level. For low-risk transactions, the system uses minimal verification (faster processing), while progressively applying stronger authentication only when necessary. This selective approach prevents unnecessary time loss during normal operations while maintaining robust security when anomalies are detected, resolving the time-security tradeoff through risk-proportional action.
Solution Approach 2:
The patent performs preliminary risk assessment continuously in the background before authentication challenges are triggered. By pre-evaluating behavioral patterns and establishing baseline trust levels, the system avoids time-consuming authentication steps for routine operations. The preliminary monitoring enables the system to quickly distinguish between normal and suspicious activities, reducing time loss by preventing unnecessary authentication interruptions.
4Reliability
If behavioral biometrics and continuous verification are used, then security reliability improves, but loss of information increases due to extensive data collection on user behavior
Solution Approach 1:
The patent extracts only the essential behavioral features needed for security verification while discarding unnecessary personal information. Rather than storing complete behavioral profiles, the system processes data to extract specific authentication-relevant patterns (timing, motion characteristics, interaction sequences) and discards redundant information. This extraction approach maintains security reliability through behavioral analysis while minimizing data retention and privacy risks.
Solution Approach 2:
The patent introduces cryptographic intermediaries that process behavioral data without exposing raw information. The system uses secure enclaves and encrypted processing pipelines where behavioral biometrics are transformed into authentication tokens or scores that verify identity without revealing underlying personal data. This intermediary layer preserves security reliability through comprehensive verification while protecting user privacy by preventing direct access to sensitive behavioral information.
Data Source
AI summary
A system and method for biobehavioral identification may include a user device, a secure system/client device, and a server. The elements of the system work together to monitor the biologic features (e.g., fingerprints, pupils, or the like) and behavior (e.g., wake time, exercise time, location) to verify the authenticity of a user requesting access to a database and/or secure facility.