LLM Embeddings for IoT Policy Recommendation and Security
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Solution Overview
Problem
Existing solutions for IoT policy rule recommendations are inadequate in addressing the technical challenges of noisy data, varying behavior patterns, and the need for dynamic and robust security measures in network environments with diverse IoT devices.
Innovation Solution
The implementation of Large Language Model (LLM) embeddings and clustering techniques for global behavior learning, which enables the automatic generation of security policy rule recommendations by identifying common behaviors, removing noisy samples, and verifying behavior patterns using an LLM classifier.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional approaches to malware detection are used, then existing security measures can be maintained, but detection accuracy and adaptability to evolving malware techniques deteriorate
Solution Approach 1:
The patent transforms network traffic data into textual representations and processes them through LLM embeddings to generate behavioral profiles. This parameter transformation from raw network data to semantic embeddings enables the system to detect malware based on behavioral patterns rather than traditional signatures, improving both detection accuracy and adaptability to new malware variants.
Solution Approach 2:
The patent replaces traditional mechanical signature-based detection systems with an AI-driven LLM-based behavioral analysis system. This substitution allows the system to understand complex network behaviors and detect malware techniques that evade traditional signatures, thereby improving reliability while maintaining adaptability.
2Ease of manufacture
If existing security approaches are applied universally, then implementation simplicity is maintained, but effectiveness across diverse computing environments deteriorates
Solution Approach 1:
The patent generates device-specific behavioral profiles by analyzing network traffic patterns unique to each device. This local quality approach allows the system to adapt security measures to the specific characteristics of each device while maintaining a unified AI-based framework, thus preserving implementation simplicity while improving security effectiveness across diverse environments.
Solution Approach 2:
The patent employs a universal LLM-based framework that can analyze diverse network traffic patterns across multiple device types and computing environments. This multi-functional approach enables the same system to effectively secure different devices without requiring device-specific custom solutions, maintaining simplicity while enhancing effectiveness.
3Measurement precision
If comprehensive behavior analysis is performed on all IoT devices, then detection accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary action by pre-processing network traffic data into textual representations and generating behavioral profiles during off-peak times or in advance. This allows the system to prepare analysis results beforehand, reducing real-time processing requirements while maintaining high detection accuracy.
Solution Approach 2:
The patent applies partial action by focusing LLM-based deep analysis only on critical network traffic patterns and high-risk behaviors, rather than exhaustively analyzing all traffic. This selective approach maintains measurement precision for important detections while significantly reducing overall processing time and computational resource consumption.
Data Source
AI summary
Techniques for IoT policy recommendation LLM embeddings based on global behavior learning are disclosed. In some embodiments, a system, process, and/or computer program product for IoT policy recommendation LLM embeddings based on global behavior learning includes receiving information associated with network communications of a plurality of Internet of Things (IoT) devices; automatically learning a global common behavior for the plurality of IoT devices using a Large Language Model (LLM) classifier to generate a plurality of recommended rules; and applying a policy to at least one of the plurality of IoT devices based on one or more of the plurality of recommended rules.