Stratified Aircraft Access Control via ML Classifiers
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Solution Overview
Problem
Current aircraft security systems lack effective stratification, allowing individuals with physical access to exert unlimited control, regardless of their role, posing a risk of unauthorized operations.
Innovation Solution
A system featuring an aircraft-based authentication and authorization unit with machine learning classifiers, utilizing historic data to verify employee access and restrict aircraft operations based on authorized levels, incorporating multiple machine learning classifiers trained on different data corpora for enhanced security decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional physical access control is used for aircraft, then ease of operation is improved, but security is worsened due to unlimited control by anyone with physical access
Solution Approach 1:
The patent segments aircraft access control into multiple hierarchical levels ( Levels 1-5) based on employee roles and responsibilities. Each level grants specific authorized operations, transforming the single undifferentiated access control into stratified granular control. This resolves the contradiction by maintaining ease of access for authorized personnel while preventing unauthorized operations through role-based restrictions.
Solution Approach 2:
The system applies different access permissions to different aircraft systems and functions based on employee role. For example, mechanics may access maintenance systems but not flight controls, while pilots have flight control access but limited maintenance access. This local differentiation of access quality enables easy operation for each employee within their authorized scope while maintaining overall security.
2Reliability
If machine learning classifiers are implemented for access control, then security is improved through accurate access level determination, but device complexity is worsened
Solution Approach 1:
The machine learning classifiers are pre-trained offline using historical aircraft data and employee data before deployment. This preliminary training action allows the system to make rapid real-time access decisions without complex runtime computation, reducing operational complexity while maintaining high security through accurate pre-computed access level determinations.
Solution Approach 2:
The patent introduces mobile access devices as intermediaries between employees and the aircraft authentication system. These devices contain embedded classifiers that perform local access assessments, reducing the computational burden on the central aircraft system while maintaining security. The intermediary handles complex processing remotely, simplifying the core aircraft system.
3Measurement precision
If multiple machine learning classifiers are used for different data corpora, then measurement precision is improved for access authorization, but device complexity is worsened
Solution Approach 1:
The patent segments the access control decision-making process into multiple specialized classifiers, each trained on specific data corpora (employee data, aircraft data, historical access data). This segmentation allows each classifier to focus on particular aspects of access determination, improving overall measurement precision while organizing complexity into manageable modular components.
Solution Approach 2:
Multiple classifier outputs are merged and combined to produce a final access authorization decision. The system integrates results from different classifiers trained on different data sources, combining their individual precision contributions into a comprehensive access determination that exceeds what any single classifier could achieve alone.
Data Source
AI summary
Techniques for enforcing stratified aircraft security are presented. The techniques are performed using an aircraft-based authentication and authorization unit and a wireless transceiver, where the authentication and authorization unit includes an electronically stored machine learning classifier. The techniques include: receiving and verifying authentication data for an employee from a mobile access device; receiving employee data from the mobile access device, the employee data including at least information representing an access event of the employee with the aircraft; providing an input to the machine learning classifier, the input including at least aircraft data and the employee data; obtaining an output from the machine learning classifier, the output indicating a level of access authorized; and providing an alert in response to a level of access by the at least one employee for the access event exceeding the level of access authorized.