The Cynicism of Modern Cybercrime: Automating the Analysis of Surface Web Marketplaces

TL;DR

This paper presents an automated machine learning pipeline to analyze surface web marketplaces used for cybercrime, revealing ongoing illegal activities and the lack of effective takedown mechanisms.

Contribution

It introduces a novel automated approach for longitudinal analysis of surface web cybercrime marketplaces, enabling rapid and accurate data extraction.

Findings

Illegal trading persists in surface web marketplaces.

Current takedown mechanisms are ineffective.

Automated analysis can identify cybercriminal activities.

Abstract

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.