Login Engine Using Multi-Sensor Data for Adaptive Authentication
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Solution Overview
Problem
Current authentication methods, including single-factor authentication and biometrics, are inadequate for high-risk transactions, as they fail to provide sufficient security against identity theft and account fraud, especially in financial sectors where risk-based assessments indicate a need for more robust protection.
Innovation Solution
A login engine system that utilizes a combination of sensors to collect and analyze data from various physical properties, such as audio, visual, and motion, to generate individual characteristic and content data, which are then compared to user profiles to authenticate users through a multi-factor authentication process, ensuring synchronicity between different sensor data to enhance security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If single-factor authentication (ID/password) is used, then ease of operation is improved, but security is worsened
Solution Approach 1:
The authentication system is segmented into multiple independent factors (knowledge-based password, possession-based device, biometric characteristics). Each factor operates independently and can be evaluated separately, allowing the system to assess security risk at each stage and require additional factors only when necessary, thus maintaining ease of operation for low-risk scenarios while enhancing security for high-risk ones.
Solution Approach 2:
The authentication requirements dynamically adjust based on risk assessment. The system evaluates the login request in real-time and determines whether single-factor or multi-factor authentication is needed. This dynamic adaptation allows the system to maintain ease of operation when risk is low while automatically strengthening security when risk indicators are present.
2Reliability
If multi-factor authentication is implemented, then security is improved, but device complexity is worsened
Solution Approach 1:
The system performs preliminary risk assessment by analyzing device characteristics, login patterns, and environmental factors before requiring full multi-factor authentication. This preliminary action allows the system to determine early whether simplified authentication is sufficient, thereby reducing the actual complexity users experience while maintaining security for high-risk scenarios.
Solution Approach 2:
The patent introduces an intermediary risk assessment module that sits between the authentication request and the authentication factors. This intermediary evaluates the request and selectively activates additional authentication factors only when needed, mediating between security requirements and user convenience, thereby reducing overall system complexity while maintaining security.
3Reliability
If biometric security is added to username and password, then security is improved, but ease of operation is worsened
Solution Approach 1:
The system dynamically determines whether biometric authentication is required based on risk assessment. For low-risk login scenarios from recognized devices and locations, the system accepts traditional username/password authentication alone. Biometric authentication is activated only when risk indicators suggest potential fraud, thereby maintaining ease of operation for legitimate users while enhancing security when needed.
Solution Approach 2:
The system uses device-based sensors and characteristics to automatically assess risk and determine authentication requirements without user intervention. The risk assessment is performed self-service style, analyzing device fingerprints, login patterns, and environmental data to decide whether additional authentication factors are needed, reducing the operational burden on users.
4Measurement precision
If sensor data collection and analysis is performed, then measurement precision is improved, but use of energy is worsened
Solution Approach 1:
The system performs partial sensor data collection and analysis only when risk indicators suggest potential fraud. Instead of continuously analyzing all sensor data from all devices, the system selectively applies deep analysis only to suspicious login attempts, thereby reducing overall energy consumption while maintaining high measurement precision when security assessment is critical.
Solution Approach 2:
The system performs preliminary risk assessment using lightweight device characteristics and login metadata before initiating energy-intensive sensor data collection and analysis. This preliminary action filters out low-risk scenarios early, preventing unnecessary energy consumption while ensuring that detailed sensor analysis is applied only when measurement precision is actually needed for security assessment.
Data Source
AI summary
A login engine for a network device can operate to secure or determine login access in response to a login request according to different sensor data of one or more physical properties. In response to the login request, a first sensor related to a first physical property can operate to detect individual characteristic data and content data. The individual characteristic data can be compared other individual characteristic data of a user profile, which can be stored in a data store. The content data can also be compared to other content data of the user profile. Based on these comparisons satisfying predetermined thresholds, a successful login stage of a plurality of login stages can be enabled.