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International Conference on Emerging Internetworking, Data & Web Technologies

EIDWT 2017: Advances in Internetworking, Data & Web Technologies pp 89–97Cite as

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

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Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((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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Author information

Authors and Affiliations

  1. Department of Information and Computer Engineering, Okayama University of Science (OUS), 1-1 Ridaicho, Kita-ku, Okayama, 700–0005, Japan

    Tetsuya Oda

  2. Department of Information and Communication Engineering, Fukuoka Institute of Technology (FIT), 3-30-1 Wajiro-Higashi, Higashi-Ku, Fukuoka, 811–0295, Japan

    Makoto Ikeda & Leonard Barolli

  3. Graduate School of Engineering, Fukuoka Institute of Technology (FIT), 3-30-1 Wajiro-Higashi, Higashi-Ku, Fukuoka, 811–0295, Japan

    Miralda Cuka & Ryoichiro Obukata

Authors
  1. Tetsuya Oda
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  2. Miralda Cuka
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  3. Ryoichiro Obukata
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  4. Makoto Ikeda
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  5. Leonard Barolli
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Corresponding author

Correspondence to Tetsuya Oda .

Editor information

Editors and Affiliations

  1. Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. School of Computer Sciences, Hubei University of Technology, Wuhan, China

    Mingwu Zhang

  3. Department of Electronic Technology, Key, Engineering University of CAPF, Xi’an, Xizang, China

    Xu An Wang

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Oda, T., Cuka, M., Obukata, R., Ikeda, M., Barolli, L. (2018). A User Prediction and Identification System for Tor Networks Using ARIMA Model. In: Barolli, L., Zhang, M., Wang, X. (eds) Advances in Internetworking, Data & Web Technologies. EIDWT 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-319-59463-7_9

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  • DOI: https://doi.org/10.1007/978-3-319-59463-7_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59462-0

  • Online ISBN: 978-3-319-59463-7

  • eBook Packages: EngineeringEngineering (R0)

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