Application of Neural Networks for Intrusion Detection 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 Neural Networks (NNs) for intrusion detection in Tor networks. We used the Back propagation NN and constructed a Tor server and a Deep Web browser (client). 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 then use the Back propagation NN to make the approximation. The simulation results show that our simulation system has a good approximation and can be used for intrusion detection in To networks.