Feature Extraction for Fake Traffic Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing solutions for identifying fake traffic fail to accurately determine feature information of network access traffic data, leading to insufficient or excessive data transmission, which hampers server terminals' ability to accurately identify fake traffic and results in performance bottlenecks.
Innovation Solution
A method involving a machine learning process to generate feature data from network access traffic, which is then used by a server to identify fake traffic, reducing data volume and improving accuracy through pre-trained models and statistical analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed data of network access traffic is sent to the server terminal, then the accuracy of fake traffic identification is improved, but the bandwidth requirement and data storage increase significantly
Solution Approach 1:
The patent extracts only the essential feature information from network access traffic data before transmission. Instead of sending complete detailed data, the system identifies and transmits only the critical features that are necessary for fake traffic identification, thereby reducing data transmission volume while maintaining identification accuracy.
Solution Approach 2:
The patent segments the network access traffic data into distinct feature components. By dividing the data into specific feature elements that can be independently analyzed, the system can transmit only the relevant segments required for identification, reducing overall data transmission requirements.
2Quantity of substance
If feature information of network access traffic is not accurately determined, then the data transmission volume is reduced, but the fake traffic identification accuracy deteriorates
Solution Approach 1:
The patent changes the parameters of data representation by transforming raw network traffic data into extracted feature parameters. This transformation allows the system to work with modified data characteristics that maintain identification capability while reducing data volume. The feature extraction process identifies which parameters are most discriminative for fake traffic detection.
Solution Approach 2:
The patent performs preliminary feature extraction and analysis before data transmission. By pre-processing the network traffic data to identify and extract relevant features in advance, the system prepares the data in a form that is optimized for subsequent identification tasks, ensuring that only the most relevant information is transmitted and processed.
3Device complexity
If access content and means information is extracted and transmitted, then the identification process is simplified, but the ability to distinguish fake traffic from normal traffic deteriorates due to traffic refreshing technologies
Solution Approach 1:
The patent changes the parameters of analysis by moving from examining surface-level access content and means to analyzing deeper behavioral features and patterns. This parameter transformation allows the system to overcome traffic refreshing technologies that mask traditional identification markers, while still maintaining a manageable identification process through automated feature extraction.
Data Source
AI summary
Embodiments of the disclosure provide methods and apparatuses for identifying fake traffic. The method can includes: collecting access traffic data of network traffic; generating feature data of the access traffic data; and sending the feature data to a server for identifying fake traffic in accordance with the feature data.