Illicit Bitcoin transactions: challenges in getting to the who, what, when and where

Abstract

Purpose The purpose of this paper is to highlight the intelligence and investigatory challenges experienced by law enforcement agencies in discovering the identity of illicit Bitcoin users and the transactions that they perform. This paper proposes solutions to assist law enforcement agencies in piecing together the disparate and complex technical, behavioural and criminological elements that make up cybercriminal offending. Design/methodology/approach A literature review was conducted to highlight the main law enforcement challenges and discussions and examine current discourse in the areas of anonymity and attribution. The paper also looked at other research and projects that aim to identify illicit transactions involving cryptocurrencies and the darknet. Findings An optimal solution would be one which has a predictive capability and a machine learning architecture which automatically collects and analyses data from the Bitcoin blockchain and other external data sources and applies search criteria matching, indexing and clustering to identify suspicious behaviours. The implementation of a machine learning architecture would help improve results over time and would be less manpower intensive. Cyber investigators would also receive intelligence in a format and language that they understand and it would allow for intelligence-led and predictive policing rather than reactive policing. The optimal solution would be one which allows for intelligence-led, predictive policing and enables and encourages information sharing between multiple stakeholders from the law enforcement, financial intelligence units, cyber security organisations and fintech industry. This would enable the creation of red flags and behaviour models and the provision of up-to-date intelligence on the threat landscape to form a viable intelligence product for law enforcement agencies so that they can more easily get to the who, what, when and where. Originality/value The development of a functional software architecture that, in theory, could be used to detected suspicious illicit transactions on the Bitcoin network.

Blockchain in FinTech: A Mapping Study

“…The full list of the selected papers can be seen in Table 1. Electrical Engineering in Japan [24] Software Engineering and Algorithms in Intelligent Systems [25] Accounting and Finance [26] Journal of Economics and Business [27] Production and Operations Management [28] Quality-Access to Success [29,30] European Business Organization Law Review [31] NMIMS Management Review [9] Strategic Change-Briefings in Entrepreneurial Finance [32] International Journal of Environmental Research and Public Health [33] Proceedings of The International Conference on Business Excellence [34] Technological Forecasting and Social Change [35] 2018 IEEE International Conference on Intelligence and Security Informatics (ISI) [36] Journal of Investment Management [37] Blockchain Technology: Platforms, Tools and Use Cases [38] Handbook of Blockchain, Digital Finance, and Inclusion, Vol 1: Cryptocurrency, Fintech, Insurtech, and Regulation [39] 2018 9th IFIP International Conference on New Technologies, Mobility, and Security (NTMS) [40] Journal of Money Laundering Control [41] Australian Feminist Studies [42] Internet Science, INSCI 2017 [43] Emerging Markets Finance and Trade [44] Financial and Credit Activity-Problems of Theory and Practice [45] 2018 6th International Symposium on Digital Forensics and Security (ISDFS) [46] Journal of Management Information Systems [47] Journal of Risk Finance [48,49] Review of International Business and Strategy [50] International Journal [51] Financial Innovation [52] Business Horizons [53] Enfoque UTE [54] Computer [55] Symmetry-Basel…”

“…Irwin et al [41] point out that a solution could come from policies that would support data sharing between different members from the law enforcement, intelligence entities, and organizations dealing with cyber-security as well as the FinTech industry. The result would be the creation of behavioral models and threat landscapes that could help recognize the identities behind the attacks.…”

“…The downturn is that if the information in one or more nodes was fraudulent, the system would collapse as nobody would have a full copy of the ledger and verification would not be possible. Bitcoin Fog or Dark Wallet, which foster anonymity through a series of scripts, could be used as an alternative [41].…”

“…However, the darknet goes beyond common users, as it has been estimated that only one out of 3,000 web pages are visible to everyday search engines, and between 80% to 98% of the information on the Internet resides in the darknet. The degree of security of this net, accessible using, for example, a Tor Browser, is extremely high, and recurrent changes in the codes and the use of techniques of indexing of search engines create a place for any kind of illegal activity [41].…”

Analysis Techniques for Illicit Bitcoin Transactions

“…Reclamation of these stolen funds is identified as a major risk to users by Tu and Meredith (2015). Irwin and Turner (2018) argue that cryptocurrency systems, in contrast with traditional money transmission businesses and financial institutions, are relatively unhindered by anti-money laundering and counter-terrorism financing (AML/CTF) regulations. In addition, these systems do not collect the necessary Personal Identifiable Information (PII) that will allow for the implementation of strict financial transaction reporting procedures for the purposes of mitigating illicit financial activity and the misappropriation of funds (Irwin and Turner, 2018).…”