Objective
This study examines Bitcoin generator (BG) websites on the clear and dark web. It focuses on their prevalence, revenue, and associated warnings, as these sites are suspected scams.

Method
Data for the study was gathered from the Dark Web Monitor and Iknaio Cryptoasset Analytics. A four-step process was used to identify BG sites and their Bitcoin addresses from 2 million dark websites.

Results
We found 832 dark web BG sites. The monetary revenue from a dark web BG site is approximately 1/3 smaller per Bitcoin address than from a clear web BG site. There is a concentration of revenue at a few BG sites. Only 24% of Bitcoin addresses on dark web BG sites have ever had money deposited on them. On the dark web, the top three clusters of crypto addresses account for 35% of the total revenue. On the clear web, the top three clusters account for 52% of the total revenue. The longer BG sites are online, the higher the revenue. There are hardly any warnings against BG sites.

Conclusion
Our results fit the Rational Choice model of crime: the revenue is modest, but the effort of the offenders is also limited.

The Dark Web, primarily Tor, has evolved to protect user privacy and freedom of speech through anonymous routing. However, Tor also facilitates cybercriminal actors who utilize it for illicit activities. Quantifying the size and nature of such activity is challenging, as Tor complicates indexing by design. This paper proposes a methodology to estimate both size and nature of illicit commercial activity on the Dark Web. We demonstrate this based on crawling Tor for single-vendor Dark Web Shops, i.e., niche storefronts operated by single cybercriminal actors or small groups. Based on data collected from Tor, we show that just in 2021, Dark Web Shops generated at least 113 million USD in revenue. Sexual abuse is the top illicit revenue category, followed by financial crime at a great distance. We also compare Dark Web Shops’ activity with a large Dark Web Marketplace, showing that these are parallel economies. Our methodology contributes towards automated analysis of illicit activity in Tor. Furthermore our analysis sheds light on the evolving Dark Web Shop ecosystem and provides insights into evidence-based policymaking regarding criminal Dark Web activity.

Open data promotes transparency and accountability as everyone can analyse it. Law enforcement and the judiciary are increasingly making data available, to increase trust and confidence in the criminal justice system. Due to privacy legislation, judicial open data — like court judgements — in Europe is usually anonymized. And even if the court judgement has been made public, the rest of the case file is usually not published. Therefore, the question arises to what extent criminological research into sentencing can make use of anonymized open data. We answer this question based on a case study in which we use the open data of the Dutch criminal justice system that is available on https://www.rechtspraak.nl/Uitspraken. Over the period 2015–2020, we analysed sentencing in 25,366 court judgements and investigated the relationship between sentence severity and the offender’s use of advanced Information and Communication Technology (ICT). The most important results are, firstly, that offenders who use advanced ICT are sentenced to longer custodial sentences compared to other offenders. Secondly, sentencing research with open data is found to be feasible.