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        <title>Classifying Illegal Activities on Tor Network Based on Web Textual Contents</title>
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        <namePart type="given">Mhd</namePart>
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    <name type="personal">
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    <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.</abstract>
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%0 Conference Proceedings
%T Classifying Illegal Activities on Tor Network Based on Web Textual Contents
%A Al Nabki, Mhd Wesam
%A Fidalgo, Eduardo
%A Alegre, Enrique
%A de Paz, Ivan
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F al-nabki-etal-2017-classifying
%X 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.
%U https://aclanthology.org/E17-1004
%P 35-43

Markdown (Informal)

[Classifying Illegal Activities on Tor Network Based on Web Textual Contents](https://aclanthology.org/E17-1004) (Al Nabki et al., EACL 2017)

• Classifying Illegal Activities on Tor Network Based on Web Textual Contents (Al Nabki et al., EACL 2017)

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.