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An Ensemble Approach to Identify Firearm Listing on Tor Hidden-Services

Hashem Alyami1, Mohd Faizan2, Wael Alosaimi3, Abdullah Alharbi3, Abhishek Kumar Pandey2, Md Tarique Jamal Ansari4, Alka Agrawal2, Raees Ahmad Khan2,*

1 Department of Computer Science, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif 21944, Saudi Arabia
2 Department of Information Technology, Babasaheb Bhimrao Ambedkar University, Lucknow, 226025, Uttar Pradesh, India
3 Department of Information Technology, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif 21944, Saudi Arabia
4 Department of Computer Application, Integral University, Lucknow, 226026, Uttar Pradesh, India

* Corresponding Author: Raees Ahmad Khan. Email: email

Computer Systems Science and Engineering 2021, 38(2), 141-149. https://doi.org/10.32604/csse.2021.017039

Abstract

The ubiquitous nature of the internet has made it easier for criminals to carry out illegal activities online. The sale of illegal firearms and weaponry on dark web cryptomarkets is one such example of it. To aid the law enforcement agencies in curbing the illicit trade of firearms on cryptomarkets, this paper has proposed an automated technique employing ensemble machine learning models to detect the firearms listings on cryptomarkets. In this work, we have used part-of-speech (PoS) tagged features in conjunction with n-gram models to construct the feature set for the ensemble model. We studied the effectiveness of the proposed features in the performance of the classification model and the relative change in the dimensionality of the feature set. The experiments and evaluations are performed on the data belonging to the three popular cryptomarkets on the Tor dark web from a publicly available dataset. The prediction of the classification model can be utilized to identify the key vendors in the ecosystem of the illegal trade of firearms. This information can then be used by law enforcement agencies to bust firearm trafficking on the dark web.

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Cite This Article

APA Style
Alyami, H., Faizan, M., Alosaimi, W., Alharbi, A., Pandey, A.K. et al. (2021). An ensemble approach to identify firearm listing on tor hidden-services. Computer Systems Science and Engineering, 38(2), 141-149. https://doi.org/10.32604/csse.2021.017039
Vancouver Style
Alyami H, Faizan M, Alosaimi W, Alharbi A, Pandey AK, Ansari MTJ, et al. An ensemble approach to identify firearm listing on tor hidden-services. Comput Syst Sci Eng. 2021;38(2):141-149 https://doi.org/10.32604/csse.2021.017039
IEEE Style
H. Alyami et al., "An Ensemble Approach to Identify Firearm Listing on Tor Hidden-Services," Comput. Syst. Sci. Eng., vol. 38, no. 2, pp. 141-149. 2021. https://doi.org/10.32604/csse.2021.017039



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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