Encrypted Video Identification via QUIC Packet Feature Extraction
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Solution Overview
Problem
Network operators and ISPs face challenges in optimizing customer experience for real-time entertainment services like video and gaming due to limited visibility caused by increased encryption use, including end-to-end encryption and services like VPNs, which hinder their ability to understand and improve network performance.
Innovation Solution
A system and method that utilizes machine learning algorithms, specifically trained neural networks, to identify encrypted video content transported over the QUIC protocol by extracting features from data packets and analyzing network conditions, allowing for accurate detection and reconstruction of video chunks despite the complexities introduced by QUIC's design and varying network conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If end-to-end encryption and VPN services are used to protect customer privacy, then customer data security is improved, but network visibility and ability to optimize customer experience deteriorate
Solution Approach 1:
The patent introduces an intermediary system that sits between the encrypted traffic and the network operator's analysis tools. This intermediary uses machine learning models to decrypt and analyze traffic patterns without requiring full decryption of customer data, thereby maintaining privacy while enabling network optimization. The system acts as a mediator that preserves both security and visibility.
Solution Approach 2:
The patent changes the analysis parameters from content-based (requiring decryption) to metadata-based (analyzing packet timing, size, frequency, and flow patterns). By shifting to analyzing different parameters that don't require breaking encryption, the system maintains customer privacy while gaining the visibility needed for network optimization.
2Measurement precision
If machine learning algorithms are used to identify encrypted video content, then content identification accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent segments the complex machine learning task into multiple simpler components: feature extraction from packet metadata, pattern recognition through trained models, and classification of video content. This segmentation reduces processing complexity by breaking down the overall task into manageable stages that can be executed efficiently.
Solution Approach 2:
The patent performs preliminary training of machine learning models using labeled data before deployment. This preliminary action creates pre-trained models that can quickly identify video content without requiring complex real-time analysis. The heavy computational work is done in advance, reducing the complexity of ongoing processing.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, a device having a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations of receiving a plurality of data packets captured from a network, wherein the data packets are associated with streaming video content across the network, and wherein the video content is encrypted; processing the plurality of data packets to extract features from each of the data packets in the plurality of data packets; providing the features to a trained machine learning (ML) model comprising a plurality of layers; and outputting an identification of the video content determined by the ML model. Other embodiments are disclosed.