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.
翻译:网络犯罪在数量上不断增长,而且日益复杂。目前,有各种各样的货币化和洗钱方法,在全世界建立一个庞大的地下经济。这些活动的一个明显指标是在线市场,使网络罪犯能够交易其被盗资产和服务。传统上,这些市场可以通过暗网进行,但其中一些市场已经出现在地表网中。在这项工作中,我们对一个表面网络市场进行了纵向分析。通过有针对性的网络剪切收集了信息,从而使我们能够为最广泛使用的地面网络市场确定数百个商人的概况。在这方面,我们讨论了这些市场中交易的产品、价格、可用性和兑换货币。这一分析是通过机器学习渠道自动进行的,使我们能够快速准确地提取必要的信息。我们的分析结果表明,在地面市场中利用了非法做法,在撰写报告时,没有有效的机制来抓走这些非法做法。