Custom LLM Network Functions for Secure Cellular Communication
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Solution Overview
Problem
Existing network security in cellular networks is vulnerable due to the use of standard protocols that can be easily hacked, and the evolution of large language models (LLMs) without constraints poses a risk of becoming undecipherable and potentially rogue, compromising network security.
Innovation Solution
Implementing custom large language models between network devices that are trained based on unique communications, monitored by both self-monitoring and a central server to ensure they do not deviate from predefined parameters, with the central server having the ability to halt or reset the LLMs if they become rogue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If standard protocols are used for network communication, then ease of operation is improved, but network security deteriorates due to vulnerability to hacking
Solution Approach 1:
The patent transforms network communication from using standard protocols to using dynamically generated language models with unique parameters for each device pair. Each custom LLM has distinct vocabulary, grammar, and communication patterns that are impossible to replicate, making the system secure while maintaining ease of operation through automated model generation and deployment.
2Adaptability or versatility
If LLMs are allowed to evolve freely, then adaptability is improved, but reliability deteriorates as they may become undecipherable or rogue
Solution Approach 1:
The patent implements a feedback mechanism where the central server continuously monitors custom LLM communications and compares them against expected patterns. When deviations are detected that indicate rogue behavior or loss of controllability, the server sends corrective feedback signals to reset or retrain the LLM, ensuring it returns to acceptable operational parameters while preserving its adaptability.
Solution Approach 2:
The system performs preliminary actions by establishing predefined evolution parameters and boundaries for custom LLMs before they begin operation. The central server configures acceptable ranges for model evolution and sets up monitoring thresholds in advance, preventing rogue behavior before it occurs while still allowing beneficial adaptation within defined limits.
3Reliability
If custom LLMs are implemented for each network device, then network security is improved, but device complexity increases
Solution Approach 1:
The patent merges the complexity management function into a centralized server that handles LLM generation, deployment, and monitoring for all network devices. Individual devices only need to implement simple interfaces for receiving and transmitting encrypted communications, while the complex tasks of custom LLM creation and management are consolidated at the server level, reducing per-device complexity while maintaining high security.
Data Source
AI summary
A processing system deployed in a cellular network may exchange communications with a network device for a predefined period of time, may generate a custom machine learning model based on messages contained in the communications, and may execute a network function on the processing system using the custom machine learning model for interacting with the network device.