  Cybercrime is continuously growing in numbers and becoming more
sophisticated. Currently, there are various monetisation and money laundering
methods, creating a huge, underground economy worldwide. A clear indicator of
these activities is online marketplaces which allow cybercriminals to trade
their stolen assets and services. While traditionally these marketplaces are
available through the dark web, several of them have emerged in the surface
web. In this work, we perform a longitudinal analysis of a surface web
marketplace. The information was collected through targeted web scrapping that
allowed us to identify hundreds of merchants' profiles for the most widely used
surface web marketplaces. In this regard, we discuss the products traded in
these markets, their prices, their availability, and the exchange currency.
This analysis is performed in an automated way through a machine learning-based
pipeline, allowing us to quickly and accurately extract the needed information.
The outcomes of our analysis evince that illegal practices are leveraged in
surface marketplaces and that there are not effective mechanisms towards their
takedown at the time of writing.
