Network Traffic Data Extraction for Bandwidth-Efficient Detection
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Solution Overview
Problem
Existing network traffic detection methods consume high bandwidth due to the transmission of full network traffic volumes, which can lead to unstable network operations and increased costs.
Innovation Solution
A data processing method that extracts and transmits reduced-volume target information to a traffic detection device, utilizing information extraction techniques to identify effective information for detection, thereby reducing bandwidth consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full network traffic data is transmitted to the traffic detection device, then detection accuracy is maintained, but bandwidth consumption increases and network stability deteriorates
Solution Approach 1:
The patent extracts only the necessary target information from the full network traffic data for transmission to the traffic detection device. The extraction unit identifies and extracts specific fields (such as source IP, destination IP, port numbers, protocol types, and packet sizes) that are essential for traffic detection, while filtering out redundant data. This extraction approach maintains detection accuracy by preserving critical detection parameters while significantly reducing bandwidth consumption by transmitting only a fraction of the original traffic data volume.
2Loss of information
If full network traffic data is transmitted to the traffic detection device, then complete information is available for analysis, but network operation stability deteriorates due to excessive resource occupation
Solution Approach 1:
The extraction unit selectively extracts only the target information fields that are essential for traffic detection and analysis, excluding redundant and unnecessary data from the original network traffic. This selective extraction maintains the completeness of detection-relevant information while reducing the overall data volume transmitted to the detection device, thereby preventing excessive resource occupation and maintaining network operation stability.
Solution Approach 2:
The patent applies different quality levels to different parts of the network traffic data. Critical detection fields (such as IP addresses, port numbers, and protocol types) are preserved with high fidelity, while non-critical fields are either extracted selectively or omitted entirely. This local quality approach ensures that the most important information for detection is maintained while reducing overall data transmission, thus balancing information completeness with network stability.
3Loss of energy
If reduced-volume target information is transmitted, then bandwidth consumption is reduced, but detection accuracy may be compromised
Solution Approach 1:
The extraction unit is designed to identify and extract specifically those fields that are critical for traffic detection accuracy. By focusing on essential parameters such as source and destination IP addresses, port numbers, protocol types, and packet size information, the system ensures that the extracted target information contains sufficient detail for accurate traffic analysis while significantly reducing the data volume transmitted to the detection device.
Solution Approach 2:
The patent transforms the original network traffic data by selecting and extracting specific parameters that are most relevant for detection purposes. This parameter selection process changes the data representation from complete raw traffic to a condensed set of key parameters, maintaining detection accuracy by preserving the most informative parameters while reducing overall data volume for more efficient transmission and processing.
4Quantity of substance
If information extraction is performed on network traffic data, then data volume is reduced for efficient transmission, but processing complexity increases
Solution Approach 1:
The information extraction process is segmented into distinct functional units that handle specific extraction tasks. The extraction unit is divided into multiple sub-components, each responsible for extracting specific types of information (such as IP address extraction, port number extraction, protocol type identification). This segmentation of the extraction process reduces processing complexity by organizing the extraction tasks into manageable, specialized modules rather than requiring a single complex extraction mechanism to handle all fields simultaneously.
Data Source
AI summary
A data processing method, apparatus, and computer-readable storage medium for efficient network traffic analysis. The method includes acquiring network traffic data, performing information extraction to obtain target information with reduced volume compared to the original data, and transmitting this target information to a traffic detection device to obtain detection results. For multiple pieces of network traffic data, the method can aggregate target information before transmission. The apparatus implements the method through memory storing program code and a processor executing the code. By processing extracted information rather than complete traffic data, the invention significantly improves efficiency of network traffic detection systems.