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Cryptocurrencies and Blockchain The Need for Better Tradecraft: Analytics and Visualizations Laboratory for Analytic Sciences Peter Merrill, [email protected] Cryptocurrencies (and Blockchain): A Fad or Not? Led by the meteoric rise (and subsequent slide) of Bitcoin at the end of 2017, cryptocurrencies (and the underlying blockchain technology) shifted closer to the mainstream. As of 14 November 2018, per coinmarketcap.com, there were 2,095 unique variants, resulting in a $206 billion global market cap. Each new variant promises improved decentralization, cryptographic security, and anonymity. How Do Cryptocurrencies (and Blockchains) Work? LAS Research Focus Areas Next Steps The Need for Better Tradecraft: Analytics and Visualizations LAS Partners and Collaboration GDA is applying TDA and machine learning to the characterization and tracing of cryptocurrency transactions. Next steps for research include: 1.) Expand analytics and tradecraft to associate and attribute cryptocurrency transactions; 2.) Develop novel 2D/3D visualization capabilities for blockchain transactional graphs; 3.) Explore implications of blockchain for contract and public record management. PNNL is applying hypergraph theory and mathematical modeling of transaction graphs to design novel analytics and visualization techniques. UIUC developed machine learning classifiers to discern trafficking-related activity among Bitcoin transactions. UCB applied graph theory and mathematics to the attribution of entities in the Bitcoin P2P network. Initial research focused on: Mission Requirements Assessment: Identify partners, missions, and current analytic 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 transaction types (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 enhanced anonymity make cryptocurrencies an attractive option for nefarious actors. Current forensic and investigative techniques employed against cryptocurrencies by financial and law enforcement analysts remain highly manual, time consuming, and frequently ineffective. Further, many techniques lack scalability to handle Big Data. To better identify illicit activities conducted with cryptocurrencies, track illicit transactions, and identify entities responsible, new forensic software, tools, and analytic capabilities need to be developed and deployed. In particular, capabilities must address analysts’ needs for data volume, characterization, and visualization. Data Volume: Limit the hay; make the needle easier to find. As volume/density increases, human cognition to perceive relationships significantly decreases; add pre- filter capabilities. Creation of analytics to select and correlate transaction subsets from various blockchains by specified date, time, and BTC/USD amount. Characterization: Put a name to the activity; make the pieces easier to fit together. Identify patterns and behaviors to characterize activity, like mixers; employ taint analysis and other methods. Application of topological data analysis (TDA) and machine learning. Visualization: Untangle the hairball; organize layout to make discovery easier. Analysts currently spend 80-90% of their time manually manipulating transaction graphs, applying order prior to initial analysis; model rote steps. Application of hypergraph theory and mathematical modeling to provide tailored graph structure.
