Tor Network Website Fingerprinting Using Statistical-Based Feature and Ensemble Learning of Traffic Data


KIPS Transactions on Software and Data Engineering, Vol. 9, No. 6, pp. 187-194, Jun. 2020
https://doi.org/10.3745/KTSDE.2020.9.6.187, Full Text:
Keywords: Anonymous Network, Traffic Collection, Website Fingerprinting, Ensemble Algorithm, Machine Learning
Abstract

This paper proposes a website fingerprinting method using ensemble learning over a Tor network that guarantees client anonymity and personal information. We construct a training problem for website fingerprinting from the traffic packets collected in the Tor network, and compare the performance of the website fingerprinting system using tree-based ensemble models. A training feature vector is prepared from the general information, burst, cell sequence length, and cell order that are extracted from the traffic sequence, and the features of each website are represented with a fixed length. For experimental evaluation, we define four learning problems (Wang14, BW, CWT, CWH) according to the use of website fingerprinting, and compare the performance with the support vector machine model using CUMUL feature vectors. In the experimental evaluation, the proposed statistical-based training feature representation is superior to the CUMUL feature representation except for the BW case.


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Cite this article
[IEEE Style]
J. Kim, W. Kim and D. Hwang, "Tor Network Website Fingerprinting Using Statistical-Based Feature and Ensemble Learning of Traffic Data," KIPS Transactions on Software and Data Engineering, vol. 9, no. 6, pp. 187-194, 2020. DOI: https://doi.org/10.3745/KTSDE.2020.9.6.187.

[ACM Style]
Junho Kim, Wongyum Kim, and Doosung Hwang. 2020. Tor Network Website Fingerprinting Using Statistical-Based Feature and Ensemble Learning of Traffic Data. KIPS Transactions on Software and Data Engineering, 9, 6, (2020), 187-194. DOI: https://doi.org/10.3745/KTSDE.2020.9.6.187.