Behavioral Biometric Authentication for Secure Network Access
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Solution Overview
Problem
Existing digital transaction systems are vulnerable to unauthorized access and fraudulent activities due to hostile environments caused by Denial of Service (DOS) attacks, network bots, and compromised credentials, which lead to difficulties in authenticating legitimate users and detecting abnormalities in user behavior.
Innovation Solution
A system that captures contextual factors of user entity behavior, device characteristics, and network traffic to calculate a transaction risk and confidence score, using a context-aware risk-based approach to determine whether a transaction request is approved, and employs out-of-band authentication for verification when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional authentication methods are used, then ease of operation is maintained, but security and reliability deteriorate due to unauthorized access and fraudulent activities
Solution Approach 1:
The system dynamically adjusts authentication requirements based on real-time risk assessment of user behavior and contextual factors. Instead of static authentication levels, the system adapts verification intensity to match the calculated risk score, applying stronger authentication only when anomalies are detected.
Solution Approach 2:
The patent replaces traditional mechanical authentication systems (passwords, tokens) with a behavioral biometric system that analyzes user interaction patterns, device characteristics, and contextual data. This substitution enables continuous implicit authentication without requiring explicit user actions.
2Reliability
If strict security measures are implemented to detect all abnormalities, then security is improved, but device complexity and processing requirements increase
Solution Approach 1:
The system implements partial monitoring by focusing computational resources on analyzing only the most relevant behavioral factors and contextual data points. Rather than comprehensively analyzing every possible parameter, the system selects key indicators that provide sufficient security assurance with reduced processing overhead.
Solution Approach 2:
The patent transforms complex security verification into simplified risk scoring by changing the parameter representation from multiple detailed authentication factors to a single aggregated risk score. This parameter transformation reduces system complexity while maintaining security effectiveness.
3Reliability
If continuous authentication is performed to detect fraudulent activities, then security is enhanced, but loss of time and processing overhead increase
Solution Approach 1:
The system performs continuous authentication in the background by continuously analyzing user behavior patterns and contextual factors without interrupting the transaction flow. The authentication process is embedded within the normal operation, allowing continuous security monitoring without explicit user actions or transaction delays.
Solution Approach 2:
The patent performs preliminary risk assessment by analyzing behavioral patterns and contextual data before completing transactions. By pre-evaluating risk factors during user interaction, the system prepares authentication decisions in advance, avoiding time-consuming verification processes during critical transaction moments.
Data Source
AI summary
Aspects of the disclosure provide techniques for using behavior based information for providing and restricting access to a secure website, or computer network and its assets to a user. Components of the system may include the following. Client remote computing device, network and browser unique attribute data collection and fingerprinting. Method for capturing user habits and fingerprinting with ability to detect abnormalities through AIML using mobile and wearable device applications. System for detection of normality of user behavior based on habits, and cyber transactions, device access and determining a confidence score associated with each transaction. Method for calculating individual transaction risk based on contextual factors such as user behavior, device, browser and the network traffic and request for authentication by account owner when risk greater than allowed threshold. Method and system to identify user device, browser, and behavior unique attributes, storing and later matching to infer change upon consequent transactions and measuring transaction risk through a search and match against classified set of static and dynamic attributes using a user, browser traffic, device search and match engine.