Edge Router AI Model Identifies Encrypted Applications
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Solution Overview
Problem
Existing deep packet inspection (DPI) methods for identifying software applications are costly and resource-intensive, and they struggle with encrypted traffic, requiring dedicated hardware and affecting system throughput, while also being unable to operate transparently on end-to-end encrypted channels without decryption.
Innovation Solution
Implementing an edge networking router device with a packet collector and an AI model trained in a cloud environment to identify software applications, which can process packet data to prioritize or restrict network resources based on application categories, reducing the need for costly hardware and enabling efficient operation on encrypted traffic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep packet inspection (DPI) is used to identify applications, then application identification accuracy is improved, but hardware cost and system resource consumption increase
Solution Approach 1:
The patent replaces the mechanical hardware-based DPI system with a software-based machine learning model that runs on general-purpose processors. Instead of using dedicated inspection hardware to analyze packet contents, the system uses trained ML models to classify applications based on packet metadata and behavioral patterns, eliminating the need for specialized DPI hardware while maintaining identification accuracy.
Solution Approach 2:
The patent uses packet metadata copies and behavioral patterns as proxies for full packet inspection. Rather than examining every packet in detail like traditional DPI, the system analyzes sampled metadata and traffic patterns to identify applications, reducing processing overhead and hardware requirements while maintaining effective application recognition.
2Measurement precision
If deep packet inspection (DPI) is used to identify applications, then application identification accuracy is improved, but system throughput decreases
Solution Approach 1:
The patent applies partial inspection by analyzing only selected packet metadata and behavioral patterns rather than performing complete deep inspection of all packets. The machine learning model processes sampled traffic data and key metadata fields, achieving sufficient application identification accuracy without the full processing overhead of traditional DPI, thereby maintaining higher system throughput.
Solution Approach 2:
The patent performs preliminary classification using machine learning models that are pre-trained on application traffic patterns. By having the ML model ready and trained in advance, the system can quickly classify new traffic without performing exhaustive inspection, enabling faster processing and maintaining throughput while achieving accurate application identification.
3Measurement precision
If deep packet inspection (DPI) is used on encrypted traffic, then application identification is possible, but decryption infrastructure and complexity increase
Solution Approach 1:
The patent replaces the decryption-based inspection approach with a machine learning-based classification system. Instead of decrypting encrypted traffic to inspect contents, the ML model analyzes encrypted packet metadata, traffic patterns, timing characteristics, and behavioral signatures to identify applications, eliminating the need for decryption infrastructure while maintaining identification capability.
Solution Approach 2:
The patent changes the inspection parameters from content-based analysis (requiring decryption) to metadata and behavioral pattern analysis. The ML model focuses on observable parameters such as packet timing, size patterns, flow characteristics, and metadata fields that remain visible even in encrypted traffic, enabling application identification without breaking encryption.
Data Source
AI summary
Edge networking router devices and systems for identifying a software application are described herein. One or more embodiments include an edge networking router device for identifying a software application comprising a packet collector to receive packet data in the edge networking router device and an artificial intelligence (AI) model configured to process the packet data received by the packet collector to identify the software application, wherein the artificial intelligence (AI) model is trained using a cloud entity and received from the cloud entity.