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Tim Alper
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Tim Alper
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Russian scientists say they have made another breakthrough in the fight against Bitcoin (BTC) and ETH-powered crime.
Per the Russian media outlet Naked Science, the solution is the brainchild of researchers at the Moscow Technical University of Communications and Informatics (MTSUSI).
The research team was led by Anna Vanyushina, who published her findings along with two co-authors in an academic paper.
Vanyushina et al explained that their solution involves tools that let investigators “visualize the Bitcoin blockchain.”
This could help police officers “find evidence of crimes related to cryptocurrency,” the researchers wrote.
The scientists say they can help police conduct more sophisticated transaction analysis. And this will let investigators identify “connections between network participants and analyze cash flows.”
Vanyushina et al said their tools “create graphs” that “visually show how assets are transferred between addresses.”
This can make it easier for police and prosecution officials to understand blockchain network data and combat BTC and ETH-powered crime.
The researchers say they piloted their solutions and algorithms on both the “Bitcoin and Ethereum blockchain networks.”
Researchers Used Bitcoin Node to Pilot Solution
Vanyushina et al said they tested their solution using “real data from the Bitcoin and Ethereum protocols.”
The resulting algorithm, they said, “can be adapted to various forms of blockchain data analysis” and “used to develop tools to combat criminal activity” in the crypto sector.
Andrey Osin, a co-author of the paper, noted that the solution generates directed acyclic graphs (DAGs).
In the mathematics world, a DAG is a graph with vertices and edges with no directed cycles. Here, each edge has a defined direction. Following these directions will never result in a closed loop.
DAGs, Osin noted, “help improve the quality and interpretability of visualizations.” These, he claimed “are especially important for investigations of crimes related to the use of cryptocurrency payments.”
“Graph visualization methods allow us to clearly show how assets are transferred from one crypto wallet to another on a […] blockchain. They let us achieve higher performance and scalability.”
Anna Vyacheslavovna, MTSUSI researcher
Co-author Marat Kiyamov explained that the team “set up a local Bitcoin node” to conduct its pilot, and “collected about 25.7 MB of data, including 46,700 blocks and their corresponding transactions.”
AI-powered Innovation
Kiyamov said that the team downloaded ETH data from the “Ethereum XBlock pro dataset,” including “blocks, transactions, contract, and call information.”
The researchers explained that their method allowed them to convert binary format data from the Bitcoin network to “easy-to-understand” diagrams.
Earlier this month, another group of Russian academics claimed it had developed a new AI-powered electricity meter solution that will help energy companies track down illegal crypto miners.
