# Machine Learning on Blockchain Data: A Systematic Mapping Study

arXiv: https://arxiv.org/abs/2403.17081 · HTML: https://mlsanity.com/paper/2403.17081/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 279 references that are in the map.
- First posted on arXiv 25 Mar 2024.

## Builds on (most cited first, 30 of 35)

- [Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics](https://mlsanity.com/paper/1908.02591/) (2019)
- [Who Are the Phishers? Phishing Scam Detection on Ethereum via Network Embedding](https://mlsanity.com/paper/1911.09259/) (2019)
- [Detecting Mixing Services via Mining Bitcoin Transaction Network with Hybrid Motifs](https://mlsanity.com/paper/2001.05233/) (2020)
- [SoK: Decentralized Finance (DeFi)](https://mlsanity.com/paper/2101.08778/) (2021)
- [Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity](https://mlsanity.com/paper/2005.14635/) (2020)
- [TTAGN: Temporal Transaction Aggregation Graph Network for Ethereum Phishing Scams Detection](https://mlsanity.com/paper/2204.13442/) (2022)
- [Data mining for detecting Bitcoin Ponzi schemes](https://mlsanity.com/paper/1803.00646/) (2018)
- [Anomaly Detection in Blockchain Networks: A Comprehensive Survey](https://mlsanity.com/paper/2112.06089/) (2021)
- [Dissecting Ethereum Blockchain Analytics: What We Learn from Topology and Geometry of Ethereum Graph](https://mlsanity.com/paper/1912.10105/) (2019)
- [Blockchain is Watching You: Profiling and Deanonymizing Ethereum Users](https://mlsanity.com/paper/2005.14051/) (2020)
- [Identity Inference on Blockchain using Graph Neural Network](https://mlsanity.com/paper/2104.06559/) (2021)
- [Anticipating cryptocurrency prices using machine learning](https://mlsanity.com/paper/1805.08550/) (2018)
- [An Evaluation of Bitcoin Address Classification based on Transaction History Summarization](https://mlsanity.com/paper/1903.07994/) (2019)
- [Cascading Machine Learning to Attack Bitcoin Anonymity](https://mlsanity.com/paper/1910.06560/) (2019)
- [Detecting Malicious Accounts in Permissionless Blockchains using Temporal Graph Properties](https://mlsanity.com/paper/2007.05169/) (2020)
- [Multi-Class classification of vulnerabilities in Smart Contracts using AWD-LSTM, with pre-trained encoder inspired from natural language processing](https://mlsanity.com/paper/2004.00362/) (2020)
- [Eth2Vec: Learning Contract-Wide Code Representations for Vulnerability Detection on Ethereum Smart Contracts](https://mlsanity.com/paper/2101.02377/) (2021)
- [TSGN: Transaction Subgraph Networks for Identifying Ethereum Phishing Accounts](https://mlsanity.com/paper/2104.08767/) (2021)
- [Characterizing Entities in the Bitcoin Blockchain](https://mlsanity.com/paper/1810.11956/) (2018)
- [Detecting Anomalous Cryptocurrency Transactions: an AML/CFT Application of Machine Learning-based Forensics](https://mlsanity.com/paper/2206.04803/) (2022)
- [DEFECTCHECKER: Automated Smart Contract Defect Detection by Analyzing EVM Bytecode](https://mlsanity.com/paper/2009.02663/) (2020)
- [Bitcoin Transaction Strategy Construction Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2109.14789/) (2021)
- [Tracking bitcoin users activity using community detection on a network of weak signals](https://mlsanity.com/paper/1710.08158/) (2017)
- [Leveraging Machine Learning for Multichain DeFi Fraud Detection](https://mlsanity.com/paper/2306.07972/) (2023)
- [Inferring short-term volatility indicators from Bitcoin blockchain](https://mlsanity.com/paper/1809.07856/) (2018)
- [Analyzing Hack Subnetworks in the Bitcoin Transaction Graph](https://mlsanity.com/paper/1910.13415/) (2019)
- [When Deep Learning Meets Smart Contracts](https://mlsanity.com/paper/2008.04093/) (2020)
- [Posting Bot Detection on Blockchain-based Social Media Platform using Machine Learning Techniques](https://mlsanity.com/paper/2008.12471/) (2020)
- [Identifying malicious accounts in Blockchains using Domain Names and associated temporal properties](https://mlsanity.com/paper/2106.13420/) (2021)
- [Blockchain Framework for Artificial Intelligence Computation](https://mlsanity.com/paper/2202.11264/) (2022)

## Built on by (most cited first, 1 of 1)

- [The Temporal Graph of Bitcoin Transactions](https://mlsanity.com/paper/2510.20028/) (2025)

## Abstract (arXiv)

Context: 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). Objective: The objective of this paper is to provide a comprehensive review of the state of the art on machine learning applied to blockchain 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. Method: A systematic mapping study has been conducted to identify the relevant literature. Ultimately, 159 articles were selected and classified according to various dimensions, specifically, the domain use case, the blockchain, the data, and the machine learning models. Results: The majority of the papers (49.7%) fall within the Anomaly use case. Bitcoin (47.2%) was the blockchain that drew the most attention. A dataset consisting of more than 1.000.000 data points was used by 31.4% of the papers. And Classification (46.5%) was the ML task most applied to blockchain data. Conclusion: The results confirm that ML applied to blockchain data is a relevant and a growing topic of interest both in the literature and in practice. Nevertheless, some open challenges and gaps remain, which can lead to future research directions. Specifically, we identify novel machine learning algorithms, the lack of a standardization framework, blockchain scalability issues and cross-chain interactions as areas worth exploring in the future.