Identification of illegal forum activities inside the Dark Net

Research output: Book chapter/Published conference paperConference paper

3 Citations (Scopus)

Abstract

Cybercriminal activities in the dark web can be considered one of the critical problems for societies around the world. It is possible that cybercriminals are using the Internet for criminal activities such as: trading and buying drugs, pedophilia, hiring hitmen, forgery, piracy and terrorism. In this paper, we are high-lighting the illegal activities happening on the dark side of the Internet, in particular the dark web forums. We used the Onion Router to check the web contents that users of dark web forums are posting and discussing. This paper also examines the most common activities in the illicit subjects that users are chatting about in the dark web forums. We performed affect analysis on the dark web forums, which is useful for measuring the presence of illicit subjects such as drugs, violence, forgery and piracy. The results show that there are similarities in the subjects between the dark web forums
Original languageEnglish
Title of host publication2018 International Conference on Machine Learning and Data Engineering (iCMLDE)
EditorsPhill Kyu Rhee, Md Rezaul Bashar
Place of PublicationPiscataway, NJ, USA
PublisherIEEE
Pages22-29
Number of pages8
ISBN (Print)9781728104041
DOIs
Publication statusPublished - 17 Jan 2019
Event2nd International Conference on Machine Learning and Data Engineering: iCMLDE 2018 - Western Sydney University (Parramatta campus), Sydney, Australia
Duration: 03 Dec 201807 Dec 2018
http://www.2018.icmlde.net.au/
https://ieeexplore.ieee.org/xpl/conhome/8613698/proceeding (proceedings)

Conference

Conference2nd International Conference on Machine Learning and Data Engineering
CountryAustralia
CitySydney
Period03/12/1807/12/18
Internet address

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  • Cite this

    Alnabulsi, H., & Islam, MD. R. (2019). Identification of illegal forum activities inside the Dark Net. In P. Kyu Rhee, & M. R. Bashar (Eds.), 2018 International Conference on Machine Learning and Data Engineering (iCMLDE) (pp. 22-29). IEEE. https://doi.org/10.1109/iCMLDE.2018.00015