Vehicle Multi-Factor Authentication for Reliable User Identity
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Solution Overview
Problem
Current cybersecurity measures for vehicles are inadequate in ensuring the safety and privacy of occupants by failing to reliably authenticate drivers or passengers, particularly in scenarios where biometric and non-biometric authentication methods may not function consistently.
Innovation Solution
An intelligent multi-factor authentication system that combines biometric and non-biometric data using machine learning and artificial intelligence to confirm the identity of vehicle users, even when individual authentication methods partially fail, by generating confidence scores and employing a combination of sensors and mobile device data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional single-factor authentication is used, then the system is simple to operate, but the security and reliability are insufficient
Solution Approach 1:
The patent combines multiple authentication factors (biometric data from vehicle sensors, non-biometric data from mobile devices, and contextual information) into a unified authentication system. The controller integrates these diverse data sources and uses machine learning to evaluate them collectively, achieving higher reliability than any single factor could provide alone.
Solution Approach 2:
The authentication system is designed to work with multiple types of sensors and data sources simultaneously. The vehicle's existing sensors (cameras, microphones, touchscreens) are repurposed for authentication, while also accepting data from external mobile devices. This multi-functional approach increases reliability without requiring completely new dedicated hardware.
2Reliability
If multiple authentication factors are combined, then the security is improved, but the ease of operation deteriorates
Solution Approach 1:
The authentication system operates automatically without requiring user intervention to switch between authentication methods. The controller autonomously collects biometric and non-biometric data, processes them through machine learning algorithms, and makes authentication decisions. Users simply need to present themselves to the vehicle, and the system handles the complex multi-factor evaluation transparently.
Solution Approach 2:
The system pre-configures multiple authentication factors and has them ready for simultaneous evaluation. Rather than sequentially asking users to provide different forms of identification, all relevant data sources are activated and evaluated together in real-time, streamlining the user experience while maintaining high security standards.
3Ease of operation
If biometric authentication is used, then the convenience is improved, but the reliability deteriorates in certain conditions
Solution Approach 1:
The system dynamically adjusts the weighting and requirements of different authentication factors based on environmental conditions, user history, and risk assessment. When biometric conditions are suboptimal (poor lighting, unusual angles), the machine learning model automatically increases reliance on non-biometric factors from mobile devices or additional contextual data, maintaining reliability while preserving convenience.
Solution Approach 2:
The machine learning controller acts as an intermediary that mediates between biometric and non-biometric authentication factors. Rather than requiring biometric authentication to succeed or fail in isolation, the controller integrates it with other data sources, allowing the system to compensate for biometric limitations while maintaining the convenient hands-free experience.
4Object-affected harmful factors
If comprehensive multi-factor authentication is implemented, then the security against cyber threats is improved, but the device complexity increases
Solution Approach 1:
The authentication system is segmented into distinct functional modules: biometric data collection from vehicle sensors, non-biometric data collection from mobile devices, machine learning processing, and authentication decision-making. Each module can be independently developed, tested, and updated, reducing the practical complexity of implementing and maintaining the comprehensive security system.
Data Source
AI summary
A vehicle or a mobile device within or near the vehicle can have multiple sensors to authenticate a passenger or driver of the vehicle in different ways, e.g., fingerprint, facial recognition, voice fingerprinting, iris scan, etc. Also, non-biometric factors can be used to authenticate the passenger or driver of the vehicle, e.g., MAC address, RFID, username and password, PIN, etc. In addition, a network attached security asset accessed by a vehicle can be included within the vehicle such as modem within the vehicle with authentication capabilities. Also, the authentication can be according to a zero trust framework or networking methodology. Some or all of such credentials and authentication factors or methods can fail individually, at least in part, in various conditions. An intelligent system, making use of intelligent multi-factor authentication, can combine such information to determine the identity of the passenger or driver with more reliability.