Synthetic Network Traffic Generation Using Text Prompts and Diffusion
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Solution Overview
Problem
The challenge in deploying machine learning models in computer networks is the scarcity of real-world network problem and attack examples for training, as networks are typically stable and rarely under attack.
Innovation Solution
A system that generates synthetic network traffic data using a diffusion model based on user-provided constraints, converting prompts into latent space representations to create network traffic scenarios such as attacks or failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-world network traffic data is collected for training machine learning models, then model training accuracy is improved, but data availability deteriorates because networks are typically stable and rarely under attack
Solution Approach 1:
The patent creates synthetic copies of network traffic data that replicate the characteristics of real attack and failure scenarios. A generative model trained on limited real data generates synthetic traffic samples that preserve the statistical properties and patterns of actual network problems, enabling adequate model training without requiring extensive real-world attack data
Solution Approach 2:
The system modifies traffic data parameters by adjusting characteristics such as packet size, inter-arrival times, protocol distributions, and flow patterns to simulate different attack scenarios and network failure conditions. This allows generation of diverse training data from limited real samples by varying key traffic parameters
2Quantity of substance
If synthetic network traffic data is generated to overcome data scarcity, then data availability is improved, but data generation complexity increases
Solution Approach 1:
The system performs preliminary training of the generative model using available real-world data before deployment. This pre-training phase establishes the foundational knowledge and patterns that the model will use to generate synthetic data, reducing the complexity of real-time data generation operations
Solution Approach 2:
The patent introduces a latent space as an intermediary representation between real traffic data and synthetic generated data. The generative model maps real traffic characteristics into a compressed latent representation, which then serves as the basis for generating diverse synthetic samples, simplifying the overall data generation process
3Reliability
If more real-world attack examples are collected for training, then machine learning model effectiveness is improved, but time to collect sufficient data increases
Solution Approach 1:
The system performs preliminary training of the generative model using available real-world data before deployment. This pre-training phase establishes the foundational knowledge and patterns that the model will use to generate synthetic data, reducing the complexity of real-time data generation operations
Solution Approach 2:
The generative model enables continuous generation of synthetic training data on-demand, eliminating interruptions for data collection. Unlike real data collection which requires waiting for actual attacks or failures, the synthetic data system maintains continuous operational capability by generating training samples whenever needed
Data Source
AI summary
In one implementation, a device obtains a prompt from a user that describes a particular type of network traffic by specifying one or more constraints. The device converts the prompt into a latent space representation. The device generates, based on the latent space representation, synthetic network traffic data for the particular type of network traffic using a diffusion model. The device provides the synthetic network traffic data.