SHA256X2
NOSTR FREN

Elliptic says it's already been privately using the AI model in its own work. As more evidence that the AI model is producing useful results, the researchers write that analyzing the source of funds for some suspicious transaction chains identified by the model helped them discover Bitcoin addresses controlled by a Russian dark-web market, a cryptocurrency “mixer” designed to obfuscate the trail of bitcoins on the blockchain, and a Panama-based Ponzi scheme. (Elliptic declined to identify any of those alleged criminals or services by name, telling WIRED it doesn't identify the targets of ongoing investigations.)
Perhaps more important than the practical use of the researchers' own AI model, however, is the potential of Elliptic's training data, which the researchers have published on the Google-owned machine learning and data science community site Kaggle. “Elliptic could have kept this for themselves,” says MIT's Weber. “Instead there was very much an open source ethos here of contributing something to the community that will allow everyone, even their competitors, to be better at anti-money-laundering.” Elliptic notes that the data it released is anonymized and doesn't contain any identifiers for the owners of Bitcoin addresses or even the addresses themselves, only the structural data of the “subgraphs” of transactions it tagged with its ratings of suspicion of money laundering.
https://www.wired.com/story/ai-crypto-tracing-model-money-laundering/

2024-05-05 15:26:19

Blockchain analysts have used machine learning tools for years to automate and sharpen their tools for tracing crypto funds and identifying criminal actors. In 2019, in fact, Elliptic already partnered with MIT and IBM to create a AI model for detecting suspicious money movements and released a much smaller data set of around 200,000 transactions that they had used to train it.
For this new research, by contrast, the same team of researchers took a much more ambitious approach. Rather than try to classify single transactions as legitimate or illicit, Elliptic analyzed collections of up to six transactions between Bitcoin address clusters it had already identified as illicit actors and the exchanges where those previously identified shady entities sold their crypto, positing that the patterns of transactions between criminals and their cashout points could serve as examples of money laundering behavior.
Working from that hypothesis, Elliptic assembled 122,000 of these so-called subgraphs, or patterns of known money laundering within a total data set of 200 million transactions. The research team then used that training data to create an AI model designed to recognize money laundering patterns across Bitcoin's entire blockchain.
https://www.wired.com/story/ai-crypto-tracing-model-money-laundering/

2024-05-05 11:17:35

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https://m.primal.net/IDVo.png

2024-05-05 11:17:00

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https://m.primal.net/IDVm.png

2024-04-29 09:27:04

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NO WE'RE NOT!!!

2024-04-28 10:11:07

You're all going to jail btw

2024-04-24 18:59:44

yes.