Competence of graph convolutional network in anti-money laundering in Bitcoin Blockchain
- Submitting institution
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Bournemouth University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 332544
- Type
- E - Conference contribution
- DOI
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10.1145/3409073.3409080
- Title of conference / published proceedings
- ICMLT 2020: Proceedings of the 2020 5th International Conference on Machine Learning Technologies
- First page
- 0
- Volume
- 0
- Issue
- 0
- ISSN
- -
- Open access status
- -
- Month of publication
- June
- Year of publication
- 2020
- URL
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- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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- Research group(s)
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- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper presents a new approach to automatically detect suspicious activity, anti-money laundering, in the blockchain. Even though this is a preliminary work using Bitcoin blockchain as an example, the work can be applied to blockchain in general. Blockchain is a double-edged sword technology. The technology is seen as the most secure peer-to-peer system; however, it has incentivised criminals to try to execute illicit activities across the network. Unfortunately, there are not many publications addressing the issues mentioned in the paper. This paper is one of a series of papers by the authors.
- Author contribution statement
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- Non-English
- No
- English abstract
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