AI Trustlet Network Access Filtering
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Solution Overview
Problem
Traditional network access controls in wireless networks are vulnerable to attacks and exploitation due to the sophistication of digital editing technology and wireless network technology, which can be used by malicious actors to emulate or spoof devices with desired access levels.
Innovation Solution
Implementing an artificial intelligence-based filtering system that analyzes data from devices, including silicon data, data transmissions, and biometric data, to dynamically classify and manage network access. This system uses trustlets executed within a trusted execution environment and stores AI filters in a distributed ledger to ensure trustworthiness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional identifier checks (whitelist/blacklist) are used for network access control, then access management is simple and straightforward, but the system becomes vulnerable to spoofing and digital editing attacks
Solution Approach 1:
The patent introduces an AI filter as an intermediary component between the network and devices. This filter analyzes multiple data sources (silicon data, transmission data, biometric data) to authenticate devices, acting as a mediator that prevents direct access based solely on identifiers. The AI filter serves as the intermediary that resolves the contradiction by providing enhanced security without requiring complete system redesign.
Solution Approach 2:
The authentication system combines multiple types of data (silicon data from device hardware, transmission data from communications, and biometric data from sensors) into a composite authentication profile. This composite approach, analogous to composite materials, creates a more robust and spoof-resistant authentication mechanism that maintains reliability while managing complexity through integrated analysis.
2Measurement precision
If AI-based filtering with multiple data sources is implemented, then network security and detection capability are enhanced, but system complexity and computational requirements increase
Solution Approach 1:
The patent segments the authentication process into distinct functional components: silicon data collection from hardware, transmission data collection from communications, biometric data collection from sensors, and AI-based analysis. This segmentation allows each component to be optimized independently while maintaining overall system accuracy, resolving the contradiction between precision and complexity.
Solution Approach 2:
The AI filter dynamically adapts its analysis based on the device type and context. Rather than applying static complex algorithms to all devices, the system dynamically adjusts the filtering approach, applying more intensive analysis only when necessary. This dynamic behavior maintains high measurement precision while reducing unnecessary computational complexity.
3Adaptability or versatility
If dynamic and adaptive access control is implemented, then the system can better handle sophisticated attacks, but traditional narrow access controls become insufficient
Solution Approach 1:
The patent implements a feedback mechanism where the AI filter continuously monitors device behavior and data patterns, comparing them against known attack signatures and normal behavior baselines. This feedback loop enables the system to adapt to new threats while maintaining reliability through continuous validation. The feedback mechanism resolves the contradiction by providing both adaptability to new attacks and reliability through ongoing verification.
Data Source
AI summary
Embodiments of the present disclosure are directed to systems and methods for artificial intelligence-based filtering of user devices on a wireless network. Upon a request from a user device to access a requested network service, a trustlet executed in a trusted execution environment of the user device is activated. The trustlet provides data associated with the user device. The data is used to distinguish normal from anomalous device behavior. Analysis of the data can be facilitated by an artificial intelligence module. Based on the analysis, the requested network service may be selectively authorized or prohibited. Additionally, the trustlet can be activated while a network service is being utilized by a user device to detect anomalous and potentially deceptive activity.