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Machine learning on blockchain data: A systematic mapping study
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DOI：10.1002/asi.70009.png)
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Abstract
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Blockchain technology has drawn growing attention in the literature and in practice. Blockchain technology generates considerable amounts of data and has thus been a topic of interest for machine learning (ML). The objective of this paper is to provide a comprehensive review of the state of the art on ML applied to on-chain data. This work aims to systematically identify, analyze, and classify the literature on ML applied to blockchain data. This will allow us to discover the fields where more effort should be placed in future research. A systematic mapping study has been conducted to identify the relevant literature. Ultimately, 211 articles were selected and classified according to various dimensions, specifically the domain use case, the blockchain, the data, and the ML models. The majority of the papers (43.35%) fall within the Anomaly use case. Ethereum (46.31%) was the blockchain that drew the most attention. A dataset consisting of more than 1,000,000 data points was used by (29.06%) of the papers. Classification (43.84%) was the ML task most applied to on-chain data. The results confirm that ML applied to on-chain data is a relevant and a growing topic of interest both in the literature and in practice. Researchers have studied interesting use cases such as address classification, anomaly detection, cryptocurrency price prediction, performance evaluation, and smart contract vulnerability detection. Nevertheless, some open challenges and gaps remain, which can lead to future research directions. Specifically, we identify novel ML algorithms, the lack of a standardization framework, blockchain scalability issues, and cross-chain interactions as areas worth exploring in the future.
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