Offending Concentration on the Internet: An Exploratory Analysis of Bitcoin-related Cybercrime

ABSTRACT

Crime research has repeatedly shown that small proportions of offenders are responsible for large proportions of crimes. While there is a substantial body of evidence for this ‘offending concentration’ in connection to traditional offline crime, there is limited research assessing the concentration of offending for cybercrime. This research analyzes victim reports of Bitcoin-related cybercrimes (blackmail, ransomware, sextortion, darknet market fraud, Bitcoin tumbler fraud) to illuminate the extent of cybercrime offending concentration and to identify groups of offenders involved in online crime. Our results indicate that a large proportion of cybercrimes are associated with a small number of very active Bitcoin addresses. However, Bitcoin addresses associated to high numbers of reports are not necessarily those that generate the largest financial benefits.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ More information about BictoinAbuse API can be found in https://www.bitcoinabuse.com/api-docs. The API allows downloading the ‘csv’ file with all reports within a given period (i.e., one day, 30 days, or forever). In order to download the data, it is necessary to obtain an API key first, which can be obtained freely as well.

² More information about the Blockchain API can be found in https://www.blockchain.com/api.

Additional information

Notes on contributors

David Buil-Gil

Patricia Saldaña-Taboada is a PhD Candidate and Predoctoral Fellow at the Department of Criminal Law of the University of Granada, Spain. Her research interests are in cryptocurrencies and digital crime, crypto markets, and crime and place.

Patricia Saldaña-Taboada

David Buil-Gil is a Lecturer in Quantitative Criminology at the Department of Criminology of the University of Manchester, UK, and a core member of the Manchester Centre for Digital Trust and Society at this same university. His primary research interests are in crime data modeling, victimization surveys, crime mapping, measurement error in criminological research, new methods for data collection, and open data.