Synthetic Network Traffic Data for Privacy-Safe AI Model Training
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Solution Overview
Problem
The challenges of limited real-world data availability, compliance concerns, and data privacy issues hinder effective AI and ML model training and testing, particularly in network and cloud security environments, where data is often scarce and has a short retention period.
Innovation Solution
The generation and utilization of synthetic data that accurately represents real customer data while adhering to privacy practices, using pattern learning from real network traffic data to simulate customer environments for model training, testing, and quality control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-world data is collected from customers for AI/ML model training, then model accuracy is improved, but data privacy compliance issues arise and data availability is limited
Solution Approach 1:
The patent creates synthetic data that copies the statistical properties and patterns of real network traffic data without containing actual customer information. The synthetic data generator learns from real data and produces artificial datasets that maintain the necessary characteristics for model training while eliminating privacy concerns associated with real customer data.
Solution Approach 2:
The synthetic data acts as an intermediary between real customer data and AI/ML models. Instead of directly using real data which causes privacy issues, the system uses synthetic data as a mediator that preserves the useful patterns and statistics needed for training while removing sensitive information.
2Quantity of substance
If real customer data is used for training, then more data volume is available, but data retention period is limited to predetermined time
Solution Approach 1:
The system performs preliminary learning from real data to capture statistical patterns and distributions. Once learned, these patterns can be used to generate synthetic data indefinitely without being constrained by the retention period of the original real data, effectively extending the usable data lifetime.
Solution Approach 2:
The synthetic data generator creates copies of data patterns that can be replicated and stored indefinitely. Unlike real data that must be deleted after a predetermined period, the synthetic copies maintain the necessary statistical properties while having no retention expiration.
3Object-affected harmful factors
If limited real data is available from few customers, then data privacy is protected, but model training accuracy is insufficient
Solution Approach 1:
The system changes the fundamental parameters of the data from real to synthetic. By transforming the data type while preserving statistical properties, the system maintains data privacy protection while achieving sufficient training accuracy through the generated synthetic datasets that reflect real network traffic patterns.
Solution Approach 2:
The patent generates synthetic data that copies the essential statistical properties and patterns from limited real data. This allows the system to expand the training dataset volume while maintaining privacy protection, as the synthetic copies preserve the necessary patterns without containing actual customer information.
4Quantity of substance
If more real data is collected from more customers, then training data volume increases, but compliance and privacy concerns increase
Solution Approach 1:
Instead of collecting more real data which increases privacy concerns, the system uses synthetic data generators to create additional training data by copying and replicating patterns from the limited real data available. This increases training data volume without proportionally increasing compliance and privacy concerns.
Data Source
AI summary
Systems and methods for generating and utilizing synthetic data include receiving a set of real network traffic data; generating synthetic data from the received set of real network traffic data based on patterns learned from the set of real network traffic data; and utilizing the synthetic data for any of training a machine learning model, testing a machine learning model, and configuring a customer cloud environment. The systems are adapted to generate a large amount of synthetic data from a limited set of real network traffic data. The produced synthetic data is altered in one or more ways to anonymize sensitive information present in the real data. Therefore, the systems are adapted to generate a large amount of synthetic data which accurately resembles real network traffic data while complying with data privacy practices.