Cryptocurrencies and BlockchainThe Need for Better Tradecraft: Analytics and Visualizations

Laboratory for Analytic Sciences

Peter Merrill, pcmerril@ncsu.edu

2017: The Rise of Cryptocurrencies (and Blockchain)

Led by the success of Bitcoin, the first and most-prominent variant, 2017 saw anexplosion in mainstream adoption of cryptocurrencies and the underlyingblockchain technology; all promising decentralization, cryptographic security, andanonymity. As of 3 November 2017, per coinmarketcap.com, there were 1,265unique cryptocurriencies, responsible for a global market cap of over $194 million.

How Does Cryptocurrency (and Blockchain) Work?

Research Focus Areas Next Steps

The Need for Analytics and Visualizations

Partners and Collaboration

GDA is applying topological dataanalysis and machine learning to thecharacterization of Bitcoin transactions.

In 2018, the LAS and associated partners will aim to:

1.) Expand analytics and tradecraft tocharacterize cryptocurrency transactions;

4.) Examine blockchain applications for controlof networked devices, for 2018 IOT exemplar.

2.) Develop novel 2D/3D visualizationcapabilities for blockchain transactional graphs;

3.) Explore implications of blockchain forcontract and public record management; and,

PNNL is applying hypergraph theoryand mathematical modeling ofBitcoin transaction graphs to designeffective case management suite.

UIUC developed machine learningclassifiers to discern trafficking-relatedactivity among Bitcoin transactions.

UCB applied graph theory andmathematics to the attribution ofentities in the Bitcoin P2P network.

As part of the 2017 Trafficking exemplar, research focused on:

Mission Requirements Assessment: Identify partners, missions, and currentanalytic tradecraft employed for analysis of cryptocurrency (specifically, Bitcoin)transactions. What capabilities are needed? Where do gaps exist?

Literature and Market Review: Evaluate current research on cryptocurrencies,blockchain technology, and related fields; examine current commercial tools(i.e., Chainalysis and Elliptic) for analyzing cryptocurrency transactions.Who can assist in developing intelligent solutions to stated gaps from above?

Analytic and Tradecraft Development: Develop methods to visualize,characterize, and attribute cryptocurrency transactions to particular transactiontypes (i.e., exchange), behaviors (i.e., mixing), and marketplaces (i.e., darknet).How can we streamline discovery and detect illicit activity or nefarious actors?

Source: blockgeeks.com

Promises of decentralization, cryptographic security, and anonymity make cryptocurrencies an attractive option fornefarious actors. Current investigative techniques employed against cryptocurrencies by financial and lawenforcement analysts are highly manual, time consuming, and frequently ineffective. Further, many lack scalability tohandle Big Data. To better identify illicit activities conducted with cryptocurrencies, track those illicit transactions,and identify those entities responsible, new forensic software, tools, and analytic capabilities need to be developedand deployed. In particular, capabilities must address needs for data volume, characterization, and visualization.

Data Volume: Limit the hay; make the needle easier tofind. As volume/density increases, human cognition toperceive relationships significantly decreases; add pre-filter capabilities. Example: GDA developed script toselect Bitcoin transaction subsets from blockchain byspecified date, time, and BTC/USD amount.

Characterization: Put a name to the activity; make thepieces easier to fit together. Identify patterns andbehaviors to characterize activity, like mixers; employtaint analysis and other methods. Application oftopological data analysis and machine learning.

Visualization: Untangle the hairball; organize layout to makediscovery easier. Analysts currently spend 80-90% of their timemanually manipulating transaction graphs, applying order prior toinitial analysis; model rote steps. Application of hypergraph theoryand mathematical modeling to provide tailored graph structure.