Design and Implementation of Tor Traffic Detection Algorithm Based on Federated Learning
The Tor network,a second-gen anonymous internet communication system,has often been exploited by cybercriminals for malicious activities like network attacks and fraud,creating cybersecurity threats and challenges.In response,this paper presented a Tor traffic detection method using federated learning.Current Tor traffic detection mainly relies on single-host detection,resulting in low efficiency and data-sharing challenges.By utilizing federated learning technology and the DP-SGD algorithm,this paper empowers participants to construct a global model while safeguarding user privacy,addressing data isolation.Experimental results show the model achieves 92%overall accuracy,90%precision,and 92%recall,ensuring user data privacy.Comparative experiments further confirm the model's superiority in privacy protection and classification effectiveness.
万方数据
维普
