Studying illicit drug trafficking on Darknet markets: structure and organisation from a Canadian perspective

Téléchargements

Téléchargements par mois depuis la dernière année

Broséus, J., Rhumorbarbe, D., Mireault, C., Ouellette, V., Crispino, F. et Décary-Hétu, D. (2016). Studying illicit drug trafficking on Darknet markets: structure and organisation from a Canadian perspective. Forensic Science International, 264 . pp. 7-14. ISSN 0379-0738 1872-6283 DOI 10.1016/j.forsciint.2016.02.045

[thumbnail of CRISPINO_F_95_POST.pdf]
Prévisualisation
PDF
Télécharger (946kB) | Prévisualisation

Résumé

Cryptomarkets are online marketplaces that are part of the Dark Web and mainly devoted to the sale of illicit drugs. They combine tools to ensure anonymity of participants with the delivery of products by mail to enable the development of illicit drug trafficking.Using data collected on eight cryptomarkets, this study provides an overview of the Canadian illicit drug market. It seeks to inform about the most prevalent illicit drugs vendors offer for sale and preferred destination countries. Moreover, the research gives an insight into the structure and organisation of distribution networks existing online. In particular, we provide information about how vendors are diversifying and replicating across marketplaces. We inform on the number of listings each vendor manages, the number of cryptomarkets they are active on and the products they offer.This research demonstrates the importance of online marketplaces in the context of illicit drug trafficking. It shows how the analysis of data available online may elicit knowledge on criminal activities. Such knowledge is mandatory to design efficient policy for monitoring or repressive purposes against anonymous marketplaces. Nevertheless, trafficking on Dark Net markets is difficult to analyse based only on digital data. A more holistic approach for investigating this crime problem should be developed. This should rely on a combined use and interpretation of digital and physical data within a single collaborative intelligence model. © 2016 Elsevier Ireland Ltd.

Type de document: Article
Date de dépôt: 25 mars 2020 16:58
Dernière modification: 25 mars 2020 16:59
Version du document déposé: Post-print (version corrigée et acceptée)
URI: https://depot-e.uqtr.ca/id/eprint/9184

Actions (administrateurs uniquement)

Éditer la notice Éditer la notice