Analysis Techniques for Illicit Bitcoin Transactions

Turner, Adam B.; McCombie, Stephen; Uhlmann, Allon J.

doi:10.3389/fcomp.2020.600596

Cited by 12 publications

(4 citation statements)

References 23 publications

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“…We see potential for these tools to better help criminologists understand the behavior around cryptocurrency. We were informed in heuristic design by computer science work examining transactions through theoretical and mathematical modeling (Meiklejohn et al, 2013; Turner et al, 2020; Zhang et al, 2020). Generally, the design is to consider common inputs and outputs in a transaction.…”

Section: Methodsmentioning

confidence: 99%

“…Researchers and industry professionals have developed numerous heuristics which utilize different elements of blockchain data for clustering. These data provide avenues through which to exploit the transparency of Bitcoin to gather on- and off-chain information of interest (Turner et al, 2020). Clustering heuristics allow for researchers and practitioners to draw inferences and conclusions surrounding individual and group spending patterns and the flow of money on the blockchain.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Follow the Money: Analyzing Darknet Activity Using Cryptocurrency and the Bitcoin Blockchain

Dearden

Tucker

2023

Journal of Contemporary Criminal Justice

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Transactions on the darknet are notoriously difficult to examine. Prior criminological research has generally used web scraping and qualitative text analysis to examine illegal darknet markets. One disadvantage of this process is that individuals can lie. Fortunately for researchers, the currency used for transactions on the darknet, cryptocurrency, is designed to be tracked. In this article, we examine transactions from a former darknet marketplace, AlphaBay. Using the blockchain, we examine the interconnectedness of both legal and illegal cryptocurrencies. In addition, we provide a structured approach to quantitatively examine the Bitcoin blockchain ledger, offering both the tools and our own experiences for other researchers interested in such approaches. While cybersecurity, information technology, accounting, and other disciplines can examine the financial data itself, we believe that criminologists can provide additional benefits in pattern analysis and organizing the context and theory around the transactions. Our results show that cryptocurrency transactions are generally identifiable (90%) and involve likely illegal transactions, transactions that attempt to obfuscate other transactions, and legal transactions. We end with a discussion of newer cryptocurrencies and related technology and how they will likely shape future work.

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Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Follow the Money: Analyzing Darknet Activity Using Cryptocurrency and the Bitcoin Blockchain

Dearden

Tucker

2023

Journal of Contemporary Criminal Justice

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“…are tested to this end. Various network analysis techniques have also been analyzed [9] such as use of LOF (local outlier factor). The difference between these and time series analysis is that network analysis attempts to find anomalies in behavior globally whereas time series attempts to show that two addresses originate from the same user with no mention of the nature of the transactions being made.…”

Section: Related Workmentioning

confidence: 99%

Illicit Activity Detection in Bitcoin Transactions using Timeseries Analysis

Maheshwari¹,

A²,

Shobha³

et al. 2023

IJACSA

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A key motivator for the usage of cryptocurrency such as bitcoin in illicit activity is the degree of anonymity provided by the alphanumeric addresses used in transactions. This however does not mean that anonymity is built into the system as the transactions being made are still subject to the human element. Additionally, there is around 400 Gigabytes of raw data available in the bitcoin blockchain, making it a big data problem. HPCC Systems is used in this research, which is a data intensive, open source, big data platform. This paper attempts to use timing data produced by taking the time intervals between consecutive transactions performed by an address and make an --von Mises criterion, two addresses are compared to find if they are from the same source. The BABD-13 dataset was used as a source of illegal addresses, which provided both references and test data points. The research shows that time-series data can be used to represent transactional behaviour of a user and the algorithm proposed is able to identify different addresses originating from the same user or users engaging in similar activity.

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“…The pseudo anonymity given by blockchains make them useful for illegal activities, most commonly ransomware and ponzi schemes. There are already many studies describing techniques for fraud detection [Chen et al 2018], and de-anonymisation [Nick 2015, Turner et al 2020. However, most studies focus on specific algorithms crafted for a single blockchain network.…”

Section: Context and Motivationmentioning

confidence: 99%

A Unified Data Model for Blockchains

Meyer

Mello²

2022

Anais Estendidos Do XXXVII Simpósio Brasileiro De Banco De Dados (SBBD Estendido 2022)

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The popularity of blockchains has been steadily rising since its inception in 2009. Its original focus was the creation of a distributed trustless system for a digital currency. Bitcoin was the first widely used blockchain. However, many more have been further created: Ethereum, Solana, Hyperledger Fabric, to name a few. Due to it, blockchain networks and its properties are being scrutinised by data scientists. This topic is known as blockchain analysis. However, analysis ́ tasks are hard to be accomplished over multiple blockchains. Even though most have similar concepts, each one has its own data modeled differently. As a consequence, scientists end up needing to do rework in order to apply similar analytics and algorithms in more than one blockchain network. In fact, blockchain data modeling is an open issue. This master’s thesis aims at proposing an unified model for blockchain data. For the best of our knowledge, there is no similar work in the literature.

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Analysis Techniques for Illicit Bitcoin Transactions

Abstract: This comprehensive overview of analysis techniques for illicit Bitcoin transactions addresses both technical, machine learning approaches as well as a non-technical, legal, and governance considerations. We focus on the field of ransomware countermeasures to illustrate our points.

Cited by 12 publications

References 23 publications

Follow the Money: Analyzing Darknet Activity Using Cryptocurrency and the Bitcoin Blockchain

Follow the Money: Analyzing Darknet Activity Using Cryptocurrency and the Bitcoin Blockchain

Illicit Activity Detection in Bitcoin Transactions using Timeseries Analysis

A Unified Data Model for Blockchains

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