Classifying Illegal Activities on Tor Network Based on Web Textual Contents

Mhd Wesam Al Nabki, Eduardo Fidalgo, Enrique Alegre, Ivan de Paz


Abstract
The freedom of the Deep Web offers a safe place where people can express themselves anonymously but they also can conduct illegal activities. In this paper, we present and make publicly available a new dataset for Darknet active domains, which we call ”Darknet Usage Text Addresses” (DUTA). We built DUTA by sampling the Tor network during two months and manually labeled each address into 26 classes. Using DUTA, we conducted a comparison between two well-known text representation techniques crossed by three different supervised classifiers to categorize the Tor hidden services. We also fixed the pipeline elements and identified the aspects that have a critical influence on the classification results. We found that the combination of TFIDF words representation with Logistic Regression classifier achieves 96.6% of 10 folds cross-validation accuracy and a macro F1 score of 93.7% when classifying a subset of illegal activities from DUTA. The good performance of the classifier might support potential tools to help the authorities in the detection of these activities.
Anthology ID:
E17-1004
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
Month:
April
Year:
2017
Address:
Valencia, Spain
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
35–43
Language:
URL:
https://aclanthology.org/E17-1004
DOI:
Bibkey:
Cite (ACL):
Mhd Wesam Al Nabki, Eduardo Fidalgo, Enrique Alegre, and Ivan de Paz. 2017. Classifying Illegal Activities on Tor Network Based on Web Textual Contents. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 35–43, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
Classifying Illegal Activities on Tor Network Based on Web Textual Contents (Al Nabki et al., EACL 2017)
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PDF:
https://preview.aclanthology.org/update-css-js/E17-1004.pdf