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Beyond the ‘Silk Road’: Assessing Illicit Drug Marketplaces on the Public Web

Part of the Lecture Notes in Social Networks book series (LNSN)

Abstract

Criminals take advantage of internet communications to amplify the impact of their actions and to form international criminal networks. At the same time, vast amounts of information generated by their online activities have become available for analysis. Open source web intelligence is a valuable methodology for understanding and responding to these new global criminal phenomena. Collecting data from websites, social media platforms and online discussion forums enables researchers, investigators and policy-makers to study and to develop appropriate responses to emerging threats. Automated web intelligence tools such as web crawlers can be used to extract relevant information from target websites and to map the threat landscape of criminogenic environments online. For the study presented in this chapter, we used our web-crawling software to download contents of 28 Russian online marketplaces for illicit drugs. Drug names, types, prices, quantities and geographical locations of sales were extracted and mapped to identify drug trafficking hotspots. Findings indicate such marketplaces can operate due to the ability of their clients to pay anonymously with virtual currencies (specifically Bitcoin and Qiwi) and to deliver the drugs through non-contact methods. This type of service is available in all large cities within Russia and provides to the seller with a safer and more anonymous alternative to “street-level” purchases. The method described in this study can be used to investigate and to prioritize online threats according to their location and severity.

Keywords

  • Online drug trafficking
  • Anonymous payments
  • Novel psychoactive substances

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Frank, R., Mikhaylov, A. (2020). Beyond the ‘Silk Road’: Assessing Illicit Drug Marketplaces on the Public Web. In: Tayebi, M.A., Glässer, U., Skillicorn, D.B. (eds) Open Source Intelligence and Cyber Crime. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-41251-7_4

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  • DOI: https://doi.org/10.1007/978-3-030-41251-7_4

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