| Project/Area Number |
16H07168
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| Research Category |
Grant-in-Aid for Research Activity Start-up
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| Allocation Type | Single-year Grants |
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| Research Field |
Information security
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| Research Institution | Keio University |
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Principal Investigator |
Toyoda Kentaroh 慶應義塾大学, 理工学部(矢上), 特任助教 (60723476)
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| Project Period (FY) |
2016-08-26 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥2,990,000 (Direct Cost: ¥2,300,000、Indirect Cost: ¥690,000)
Fiscal Year 2017: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2016: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | Bitcoin / データ解析 / 取引詐欺 / ブロックチェーン解析 / 仮想通貨 / 機械学習 / セキュア・ネットワーク / Fintech / 犯罪検知 / データ・マイニング / 暗号・認証等 |
|---|
| Outline of Final Research Achievements |
Bitcoin is one of the most successful decentralized cryptocurrencies to date. However, it has been reported that it can be used for investment scams, which are referred to as HYIP (High Yield Investment Programs). So far, no schemes has been proposed to detect HYIP operators' Bitcoin addresses, although it is useful from the security forensics aspect. We have proposed a novel scheme to identify HYIP operators' Bitcoin addresses by analyzing transactions history. We collected 918 HYIP operators' Bitcoin addresses from the Internet and analyzed the characteristics of transactions where the collected Bitcoin addresses are involved. Based on this analysis, we proposed a machine learning technique to classify given Bitcoin addresses into HYIP operators ones or not. By evaluating the classification performance , our best scheme achieves that 88% of HYIP addresses are correctly classified, while maintaining false positive rate less than 3.8%. We also built a web application for practice.
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