Data Flow Content Recognition via Deep Packet Inspection
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Solution Overview
Problem
In peer-to-peer networks, it is challenging to differentiate and manage data flows based on their content, particularly when they contain copyright-protected material, as existing methods rely on digital watermarks or comparisons with known data flows, which are inadequate for unknown or unmarked content.
Innovation Solution
A method that compares incoming data flows with a list of known flows and, if no match is found, analyzes administration and user data in real-time or offline to determine content type, using techniques like deep packet inspection and cross-correlation to decide processing actions, such as forwarding, corrupting, or charging for copyright-protected content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data flows are compared with a list of known data flows, then copyright-protected content can be identified, but unknown or unmarked content cannot be processed
Solution Approach 1:
The system performs preliminary comparison of data flows against a list of known copyrighted data flows before implementing full analysis. This preliminary action filters out known copyrighted content while allowing unknown content to proceed to further analysis, resolving the contradiction by handling both known and unknown content appropriately
Solution Approach 2:
The content recognition process is segmented into multiple stages: first comparing with known data flows, then analyzing administration data, and finally analyzing user data if needed. This segmentation allows the system to efficiently handle known content while having fallback mechanisms for unknown content, improving both reliability and adaptability
2Ease of operation
If digital watermarks are used to detect copyright-protected data, then content identification is simplified, but the method cannot be used for data flows without watermarks
Solution Approach 1:
The system uses administration data (headers, metadata) as an intermediary to identify content characteristics before analyzing user data. This intermediary approach allows the system to handle both watermarked and unwatermarked content by using multiple identification layers, resolving the contradiction between simplicity and versatility
Solution Approach 2:
The content analysis system is designed with multi-functionality to handle various content types and states. It can process watermarked content, unwatermarked content, and content with different characteristics through a unified approach that compares data flows against known patterns and analyzes multiple data fields, making it universally applicable
3Reliability
If data flow analysis is performed in real-time, then data transmission quality is maintained, but processing complexity increases
Solution Approach 1:
The real-time analysis process is segmented into priority-based stages: administration data analysis is performed first and continuously, while user data analysis is performed selectively based on detected characteristics. This segmentation enables real-time processing of critical metadata while managing complexity through conditional full analysis
Solution Approach 2:
The system applies partial analysis (administration data only) for most data flows to maintain real-time performance, and reserves full analysis (including user data) for suspicious or unknown content. This partial action approach maintains data transmission quality while managing processing complexity through selective deep analysis
Data Source
AI summary
A method of content-related processing of a data flow compares the data flow with a list of known data flows, and determines whether the data flow is recognized as a known data flow. When the data flow is recognized as a known data flow, the data flow is processed as specified in the list. When the data flow is not recognized as a known data flow, successively analyzing the data flow until a content type of the data flow is sufficiently recognized in order to decide how to process the data flow.