Edge ML Traffic Classification for Zero-Day Applications
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional network traffic classification systems struggle with scalability, accuracy, and adaptability due to the diverse and rapidly changing nature of network traffic patterns, particularly failing to recognize new or zero-day applications, and are constrained by system limitations when running on edge devices.
Innovation Solution
A neural network-based machine learning model is deployed on edge devices, utilizing contrastive and reinforcement learning to adaptively classify network traffic by learning patterns from historical data and distinguishing between legitimate and anomalous traffic, capable of recognizing new applications with high precision and resilience to pattern variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional classification systems rely on predefined signatures and previously observed network data, then classification accuracy for known applications is improved, but the system becomes unable to recognize new or zero-day applications
Solution Approach 1:
The system performs preliminary actions by pre-training the machine learning model on historical network traffic data from multiple applications before deployment. This pre-training enables the model to learn general traffic patterns and characteristics in advance, allowing it to adapt to new applications without requiring retraining or updates when encountering zero-day applications.
Solution Approach 2:
The classification system transitions from a static, signature-based approach to a dynamic, adaptive machine learning model. The model continuously learns and adjusts its classification behavior based on input traffic patterns, enabling it to adapt to new applications and traffic variations without manual updates, thus resolving the contradiction between maintaining accuracy for known applications and adapting to new ones.
2Speed
If classification systems are deployed on edge devices with limited compute and memory, then response time and local processing capability are improved, but the system becomes constrained in handling diverse application patterns
Solution Approach 1:
The patent changes the parameters of the classification system by transitioning from traditional signature-based methods to a machine learning model optimized for edge deployment. The model uses contrastive learning and reinforcement learning techniques that are computationally efficient, enabling accurate classification of diverse traffic patterns within the constrained memory and compute resources of edge devices.
Solution Approach 2:
The system replaces the mechanical, rule-based classification mechanism with an intelligent machine learning model that can generalize from learned patterns. This substitution enables the edge device to handle diverse application traffic without requiring extensive rule sets or large memory resources, as the model generalizes from training data rather than storing explicit classification rules for every possible traffic pattern.
3Adaptability or versatility
If the machine learning model is pre-trained on historical network traffic, then the model's ability to generalize to new applications is improved, but the training process requires significant computational resources and time
Solution Approach 1:
The system performs the computationally intensive pre-training action beforehand during system deployment or initialization, rather than requiring continuous training during operation. This preliminary training phase establishes the model's generalization capability once, allowing rapid inference and classification during actual network traffic monitoring without incurring ongoing training time losses.
Data Source
AI summary
Devices and methods for adaptively classifying network traffic associated with a new application are provided. A network device, for example, an edge device, stores a Machine Learning (ML) model pre-trained based on historical network traffic associated with a set of applications. The network device receives network traffic associated with a new application, for example, a zero-day application, that is different from the set of applications. The ML model learns one or more patterns associated with the received network traffic. The ML model detects whether the learned pattern(s) is similar to previously learned patterns of at least one application. The ML model classifies the received network traffic as legitimate traffic or anomalous traffic based on the detection. The ML model is scalable, providing timely classifications for different types of network traffic, while handling protocol and application diversity, variability in traffic patterns, and emergence of zero-day application traffic.