A Neural Network Based User Identification for Tor Networks: Data Analysis Using Friedman Test
Taro Ishitaki, Tetsuya Oda, Leonard Barolli · 2016
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 or identify the bad behavior users in Tor networks. In this paper, we present the application of Neural Networks (NNs) for user identification in Tor networks. We used the Back-propagation NN and constructed a Tor server, a Deep Web browser (Tor client) and a Surface Web browser. 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 used the Back-propagation NN to make the approximation. For evaluation we considered Number of Packets (NoP), Round Trip Time (RTT), jitter, packet loss and throughput metrics. We present many simulation results considering Tor client. We analyze the data using Friedman test. From the results, we see that for 15 hidden units the system can identify Tor client.