Machine Learning for Blockchain Data Analysis: Progress and Opportunities
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914773752348672 |
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| author | Azad, Poupak Akcora, Cuneyt Gurcan Khan, Arijit |
| author_facet | Azad, Poupak Akcora, Cuneyt Gurcan Khan, Arijit |
| contents | Blockchain technology has rapidly emerged to mainstream attention, while its publicly accessible, heterogeneous, massive-volume, and temporal data are reminiscent of the complex dynamics encountered during the last decade of big data. Unlike any prior data source, blockchain datasets encompass multiple layers of interactions across real-world entities, e.g., human users, autonomous programs, and smart contracts. Furthermore, blockchain's integration with cryptocurrencies has introduced financial aspects of unprecedented scale and complexity such as decentralized finance, stablecoins, non-fungible tokens, and central bank digital currencies. These unique characteristics present both opportunities and challenges for machine learning on blockchain data.
On one hand, we examine the state-of-the-art solutions, applications, and future directions associated with leveraging machine learning for blockchain data analysis critical for the improvement of blockchain technology such as e-crime detection and trends prediction. On the other hand, we shed light on the pivotal role of blockchain by providing vast datasets and tools that can catalyze the growth of the evolving machine learning ecosystem. This paper serves as a comprehensive resource for researchers, practitioners, and policymakers, offering a roadmap for navigating this dynamic and transformative field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_18251 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine Learning for Blockchain Data Analysis: Progress and Opportunities Azad, Poupak Akcora, Cuneyt Gurcan Khan, Arijit Cryptography and Security Machine Learning Blockchain technology has rapidly emerged to mainstream attention, while its publicly accessible, heterogeneous, massive-volume, and temporal data are reminiscent of the complex dynamics encountered during the last decade of big data. Unlike any prior data source, blockchain datasets encompass multiple layers of interactions across real-world entities, e.g., human users, autonomous programs, and smart contracts. Furthermore, blockchain's integration with cryptocurrencies has introduced financial aspects of unprecedented scale and complexity such as decentralized finance, stablecoins, non-fungible tokens, and central bank digital currencies. These unique characteristics present both opportunities and challenges for machine learning on blockchain data. On one hand, we examine the state-of-the-art solutions, applications, and future directions associated with leveraging machine learning for blockchain data analysis critical for the improvement of blockchain technology such as e-crime detection and trends prediction. On the other hand, we shed light on the pivotal role of blockchain by providing vast datasets and tools that can catalyze the growth of the evolving machine learning ecosystem. This paper serves as a comprehensive resource for researchers, practitioners, and policymakers, offering a roadmap for navigating this dynamic and transformative field. |
| title | Machine Learning for Blockchain Data Analysis: Progress and Opportunities |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2404.18251 |