Network Data Decoding via AI-Generated Application Recipes
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Solution Overview
Problem
Existing cybersecurity solutions require excessive time and manual labor to configure decoding of network data for new or altered applications, and large language models (LLMs) are computationally inefficient for real-time application identification and data decoding.
Innovation Solution
A security proxy system using field-programmable gate arrays (FPGAs) and application-specific integrated circuits (ASICs) with Generative AI (GenAI) and machine learning models to automatically infer applications and generate decoding logic, reducing configuration time and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual configuration methods are used for decoding network data, then accuracy can be maintained, but time consumption and labor requirements increase significantly
Solution Approach 1:
The system performs self-service by automatically inferring application protocols and generating decoding recipes without manual configuration. The machine learning model analyzes network traffic patterns and autonomously creates decoding logic, eliminating the need for manual setup while maintaining accurate data decoding capabilities.
Solution Approach 2:
The system performs preliminary action by pre-processing network traffic data to identify patterns and characteristics before generating decoding recipes. The machine learning model analyzes and clusters transaction data in advance, enabling rapid deployment of decoding capabilities for new applications without manual intervention.
2Measurement precision
If large language models are used for application identification, then comprehensive analysis is achieved, but computational efficiency decreases for real-time processing
Solution Approach 1:
The system extracts only the essential features and patterns from network traffic data using machine learning clustering, rather than processing entire datasets through comprehensive language models. By extracting key transaction characteristics and organizing them into structured representations, the system achieves accurate application identification with reduced computational overhead.
Solution Approach 2:
The system segments the complex task of application identification into multiple stages: initial traffic analysis, pattern clustering, and recipe generation. This segmentation allows the system to process data in manageable portions, improving real-time performance while maintaining identification accuracy through specialized decoding recipes for each application type.
3Manufacturing precision
If comprehensive decoding recipes are generated for all applications, then decoding accuracy is improved, but system complexity increases
Solution Approach 1:
The system applies local quality by generating application-specific decoding recipes tailored to the unique characteristics of each application protocol. Rather than using a single complex decoding system for all applications, the system creates specialized recipes for each application type based on analyzed traffic patterns, improving decoding accuracy while keeping each individual recipe relatively simple and manageable.
Data Source
AI summary
A method includes: accessing, at a security proxy, a first transaction of a communications session between a client device and an application hosted by a server; clustering the first transaction with a plurality of other transactions for the application, based on a common data feature of the first transaction and the plurality of other transactions; providing structured data including indications of the clustering and the common data feature as input to a natural language processing (NLP) machine learning model; and based on one or more outputs of the NLP machine learning model, identifying the application, identifying a content type of a portion of data in the first transaction, and, based on the identified content type, generating a recipe for decoding network data corresponding to the application.