A User Prediction and Identification System for Tor Networks Using ARIMA Model

  • Tetsuya Oda
  • Miralda Cuka
  • Ryoichiro Obukata
  • Makoto Ikeda
  • Leonard Barolli
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 6)

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 Autoregression Integrated Moving Average (ARIMA) 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 then used the ARIMA model to make the prediction. The simulation results show that proposed system has a good prediction of user behavior in Tor networks.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Tetsuya Oda
    • 1
  • Miralda Cuka
    • 3
  • Ryoichiro Obukata
    • 3
  • Makoto Ikeda
    • 2
  • Leonard Barolli
    • 2
  1. 1.Department of Information and Computer EngineeringOkayama University of Science (OUS)OkayamaJapan
  2. 2.Department of Information and Communication EngineeringFukuoka Institute of Technology (FIT)FukuokaJapan
  3. 3.Graduate School of EngineeringFukuoka Institute of Technology (FIT)FukuokaJapan

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