Synthetic Traffic Generation for ML Network Training
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Solution Overview
Problem
Existing solutions for training and validating machine learning algorithms in network traffic analysis are limited by the need for labeled datasets, reliance on real traffic conditions, and inability to generate synthetic traffic, which restricts their scalability and versatility in handling changing network environments and encrypted traffic.
Innovation Solution
A method and system that generates synthetic traffic patterns and integrates them with real network traffic for training and validation, using a labelling component to create labeled datasets and a feature extraction module to enhance the training process, allowing for dynamic adaptation and real-time feature generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If synthetic traffic is generated and integrated with real network traffic for training, then the versatility and adaptability of machine learning algorithms is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The system segments the training data into synthetic traffic components and real traffic components, processing them through separate but coordinated modules. The synthetic traffic generation, injection, and integration are handled as distinct functional segments that can be independently configured and managed, reducing overall system complexity while maintaining versatility.
Solution Approach 2:
The patent introduces intermediary components such as the traffic injection module and data integration module that mediate between synthetic traffic generation and the machine learning training process. These intermediaries manage the complexity of integrating synthetic and real traffic by providing standardized interfaces and controlled interaction points.
2Productivity
If labeled datasets are created through automated labelling components, then the productivity of training data preparation is improved, but the measurement precision and accuracy of labels may be compromised
Solution Approach 1:
The system implements feedback mechanisms where the labelling component's output is continuously evaluated and refined. The integration module compares automated labels with ground truth from real traffic patterns, and the system adjusts labelling strategies based on performance feedback, maintaining both productivity and precision through iterative improvement.
Solution Approach 2:
The automated labelling component operates autonomously to generate training labels at scale, while the system self-corrects through validation against real traffic patterns. This self-service approach maintains productivity by eliminating manual labelling while preserving precision through automated validation and correction mechanisms.
3Speed
If dynamic feature generation is implemented in real-time, then the responsiveness of the machine learning model is improved, but the computational load and processing time increase
Solution Approach 1:
The system performs preliminary feature extraction and preparation during the training phase using synthetic traffic, pre-computing feature relationships and patterns. This preliminary action reduces the computational burden during real-time operation, allowing dynamic feature generation with lower energy consumption while maintaining high responsiveness.
Solution Approach 2:
The patent implements partial feature generation where only the most critical and discriminative features are extracted and updated in real-time, rather than computing all possible features. This selective approach reduces computational load and energy consumption while maintaining the responsiveness needed for real-time machine learning operations.
Data Source
AI summary
A system and method for training and validating ML algorithms in real networks, including: generating synthetic traffic and receiving it along with real traffic; aggregating the received traffic into network flows by using metadata and transforming them to generate a first dataset readable by the ML algorithm, comprising features defined by the metadata; labelling the traffic and selecting a subset of the features from the labelled dataset used in an iterative training to generate a trained model; filtering out a part of real traffic to obtain a second labelled dataset; and selecting a subset of features from the second labelled dataset used for validating the trained model by comparing predicted results for the trained model and the labels; repeating the steps with a different subset of features to generate another trained model until results are positive in terms of precision or accuracy.