Application of Deep Recurrent Neural Networks for Prediction of User Behavior in Tor Networks

TLDR

Tor’s anonymity facilitates malicious activities such as DDoS and identity theft, compromising non‑repudiation and necessitating new intrusion detection models. This study applies Deep Recurrent Neural Networks to predict user behavior within Tor networks. We built a laboratory Tor server and client, captured traffic with Wireshark, and trained DRNNs to forecast user actions. Simulation results demonstrate that the DRNN model accurately predicts user behavior in Tor networks.

Abstract

Due to the amount of anonymity afforded to users of the Tor infrastructure, Tor has become a useful tool for malicious users. With Tor, the users are able to compromise the non-repudiation principle of computer security. Also, the potentially hackers may launch attacks such as DDoS or identity theft behind Tor. For this reason, there are needed new systems and models to detect the intrusion in Tor networks. In this paper, we present the application of Deep Recurrent Neural Networks (DRNNs) for prediction of user behavior in Tor networks. We constructed a Tor server and a Deep Web browser (Tor client) in our laboratory. Then, the client sends the data browsing to the Tor server using the Tor network. We used Wireshark Network Analyzer to get the data and thenused the DRNNs to make the prediction. The simulation results show that our simulation system has a good prediction of user behavior in Tor networks.