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Go See a Specialist? Predicting Cybercrime Sales on Online Anonymous Markets from Vendor and Product Characteristics

Conference Paper (2020)
Authors

R. S. van Wegberg (TU Delft - Organisation & Governance)

F.E.G. Miedema (TU Delft - Organisation & Governance)

U. Akyazi (TU Delft - Organisation & Governance)

Arman Noroozian (TU Delft - Organisation & Governance)

Bram Klievink (TU Delft - Organisation & Governance, Universiteit Leiden)

M. J.G. van Eeten (TU Delft - Organisation & Governance)

Research Group
Organisation & Governance
Copyright
© 2020 R.S. van Wegberg, F.E.G. Miedema, U. Akyazi, A. Noroozian, A.J. Klievink, M.J.G. van Eeten
To reference this document use:
https://doi.org/10.1145/3366423.3380162
More Info
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Publication Year
2020
Language
English
Copyright
© 2020 R.S. van Wegberg, F.E.G. Miedema, U. Akyazi, A. Noroozian, A.J. Klievink, M.J.G. van Eeten
Research Group
Organisation & Governance
Pages (from-to)
816-826
ISBN (electronic)
978-1-4503-7023-3
DOI:
https://doi.org/10.1145/3366423.3380162
Reuse Rights

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Abstract

Many cybercriminal entrepreneurs lack the skills and techniques to provision certain parts of their business model, leading them to outsource these parts to specialized criminal vendors. Online anonymous markets, from Silk Road to AlphaBay, have been used to search for these products and contract with their criminal vendors. While one listing of a product generates high sales numbers, another identical listing fails to sell. In this paper, we investigate which factors determine the performance of cybercrime products.
To answer this question, we analyze scraped data on the business-to-business cybercrime segments of AlphaBay (2015-2017), consist- ing of 7,543 listings from 1,339 vendors, sold at least 126,934 times. We construct new variables to capture product differentiators and price. We capture the influence of vendor characteristics by identifying five distinct vendor profiles based on latent profile analysis of six properties. We leverage these product and vendor characteristics to empirically predict the performance of cybercrime products, whilst controlling for the lifespan and type of solution. Consistent with earlier insights into carding forums, we identify prevalent product differentiators to be influencing the relative success of a product. While all these product differentiators do correlate significantly with product performance, their explanatory power is lower than that of vendor profiles. When outsourcing, the vendor seems to be of more importance to the buyers than product differentiators.