1   Large-scale network traffic collection technology

• Automated collection of network traffic generated during web service access.

 2   Mass client simultaneous control technology 

• Controlling a large number of client servers from a command and control (C&C) server. 

 3   Network traffic filtering (refinement) technology 

• Collecting only traffic that meets specific conditions (e.g., specific IPs, types, regions) during or after traffic acquisition. 

 4   AI-based network traffic analysis and service region inference 

• Identifying the service region of web services on the Tor network through deep learning and machine learning–based analysis of traffic characteristics.

 1   User deanonymization through packet traceback 

• Enable deanonymization of Tor network users by analyzing packet correlation between entry and exit nodes.

 2   Dataset augmentation 

• Enhance traffic datasets for AI training using GAN-based data augmentation. 

 3   Automated data collection via C&C server 

• Automate traffic data collection across various countries by controlling clients deployed on Xen servers and Google Cloud through a centralized C&C server.