Network Traffic Classifier Joint Learning Framework
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Solution Overview
Problem
Current network traffic classification systems face challenges in accurately representing network traffic and optimizing classifier parameters, leading to inaccuracies and high false positive/negative rates, especially in real-time and dynamic network environments.
Innovation Solution
A framework that employs kernel embedding of distributions to generate a mean map of traffic data, identifies a set of lattice points to approximate this map, and jointly learns a traffic classifier using machine learning to analyze network traffic, optimizing both the representation and classifier parameters for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional traffic classification methods are used, then the system is simple to implement, but the classification accuracy is low with high false positive/negative rates
Solution Approach 1:
The patent combines traffic representation learning and classifier learning into a single joint training process. The encoder network and classifier network are trained simultaneously with shared parameters, allowing the system to learn optimal traffic representations and classification boundaries together, thereby improving classification accuracy while maintaining a unified system architecture.
Solution Approach 2:
The patent transforms traffic data from original feature space to a latent representation space using kernel embedding and lattice point approximation. This dimensional transformation enables the classifier to operate in a more discriminative space, improving classification accuracy by capturing non-linear traffic patterns that are not apparent in the original feature space.
2Measurement precision
If complex traffic representation methods are employed, then the discriminative capability is improved, but the computational overhead increases
Solution Approach 1:
The patent uses lattice point approximation to represent the mean map, selecting a finite set of representative points rather than processing the entire continuous space. This partial action approach captures the essential traffic patterns while significantly reducing computational requirements compared to exhaustive analysis of all possible traffic features.
Solution Approach 2:
The patent replaces traditional mechanical traffic analysis methods with machine learning-based automatic feature extraction. The encoder network automatically learns relevant traffic characteristics from raw data, eliminating the need for manual feature engineering and reducing computational overhead associated with complex preprocessing pipelines.
3Productivity
If real-time classification is performed, then the network can adapt quickly to changes, but the processing time and computational resources are constrained
Solution Approach 1:
The patent performs preliminary action by pre-training the encoder network to learn optimal traffic representations before deployment. The lattice points and mean map are computed in advance during the training phase, allowing the classifier to make rapid real-time decisions without performing complex computations during actual traffic classification, thus balancing speed and accuracy.
Data Source
AI summary
In one embodiment, a device in a network receives traffic data associated with a particular communication channel between two or more nodes in the network. The device generates a mean map by employing kernel embedding of distributions to the traffic data. The device forms a representation of the communication channel by identifying a set of lattice points that approximate the mean map. The device generates a traffic classifier using the representation of the communication channel. The device uses machine learning to jointly identify the set of lattice points and one or more parameters of the traffic classifier. The device causes the traffic classifier to analyze network traffic sent via the communication channel.