Machine Learning on Blockchain Data: A Systematic Mapping Study

• URL: http://arxiv.org/abs/2403.17081v1
• Date: Mon, 25 Mar 2024 18:12:16 GMT
• Title: Machine Learning on Blockchain Data: A Systematic Mapping Study
• Authors: Georgios Palaiokrassas, Sarah Bouraga, Leandros Tassiulas,
• Abstract summary: Technology has drawn growing attention in the literature and in practice. This paper provides a comprehensive review of the state of the art on machine learning applied to blockchain data.
• Score: 9.677855897134604
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• BlockFound: Customized blockchain foundation model for anomaly detection [47.04595143348698]
  BlockFound is a customized foundation model for anomaly blockchain transaction detection. We introduce a series of customized designs to model the unique data structure of blockchain transactions. BlockFound is the only method that successfully detects anomalous transactions on Solana with high accuracy.
  arXiv  Detail & Related papers  (2024-10-05T05:11:34Z)
• SOK: Blockchain for Provenance [0.0]
  Provenance, which traces data from its creation to manipulation, is crucial for ensuring data integrity, reliability, and trustworthiness. Provenance technology has become a popular choice for implementing provenance due to its distributed, transparent, and immutable nature. Numerous studies on blockchain designs are specifically dedicated to provenance, and specialize in this area.
  arXiv  Detail & Related papers  (2024-07-25T01:46:49Z)
• IT Strategic alignment in the decentralized finance (DeFi): CBDC and digital currencies [49.1574468325115]
  Decentralized finance (DeFi) is a disruptive-based financial infrastructure. This paper seeks to answer two main questions 1) What are the common IT elements in the DeFi? And 2) How the elements to the IT strategic alignment in DeFi?
  arXiv  Detail & Related papers  (2024-05-17T10:19:20Z)
• Blockchains for Internet of Things: Fundamentals, Applications, and Challenges [38.29453164670072]
  Not every blockchain system is suitable for specific IoT applications. Public blockchains are not suitable for storing sensitive data. We explore the blockchain's application in three pivotal IoT areas: edge AI, communications, and healthcare.
  arXiv  Detail & Related papers  (2024-05-08T04:25:57Z)
• Machine Learning for Blockchain Data Analysis: Progress and Opportunities [9.07520594836878]
  blockchain datasets encompass multiple layers of interactions across real-world entities, e.g., human users, autonomous programs, and smart contracts. These unique characteristics present both opportunities and challenges for machine learning on blockchain data. This paper serves as a comprehensive resource for researchers, practitioners, and policymakers, offering a roadmap for navigating this dynamic and transformative field.
  arXiv  Detail & Related papers  (2024-04-28T17:18:08Z)
• Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning [51.13534069758711]
  Decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities. Federated Learning (FL) enables participants to collaboratively train models while safeguarding data privacy. This paper investigates the synergy between blockchain's security features and FL's privacy-preserving model training capabilities.
  arXiv  Detail & Related papers  (2024-03-28T07:08:26Z)
• Generative AI-enabled Blockchain Networks: Fundamentals, Applications, and Case Study [73.87110604150315]
  Generative Artificial Intelligence (GAI) has emerged as a promising solution to address challenges of blockchain technology. In this paper, we first introduce GAI techniques, outline their applications, and discuss existing solutions for integrating GAI into blockchains.
  arXiv  Detail & Related papers  (2024-01-28T10:46:17Z)
• Extracting Blockchain Concepts from Text [0.5520082338220947]
  The main objective of this project was to apply machine learning models to extract information from whitepapers and academic articles focused on the blockchain area to organize this information and aid users to navigate the space.
  arXiv  Detail & Related papers  (2023-05-07T00:16:30Z)
• Quantum-resistance in blockchain networks [46.63333997460008]
  This paper describes the work carried out by the Inter-American Development Bank, the IDB Lab, LACChain, Quantum Computing (CQC), and Tecnologico de Monterrey to identify and eliminate quantum threats in blockchain networks. The advent of quantum computing threatens internet protocols and blockchain networks because they utilize non-quantum resistant cryptographic algorithms.
  arXiv  Detail & Related papers  (2021-06-11T23:39:25Z)
• A Survey of State-of-the-Art on Blockchains: Theories, Modelings, and Tools [23.85653321591618]
  This survey can serve as a useful guideline for researchers, engineers, and educators about the cutting-edge development of blockchains. Several promising open issues are also summarized finally for future research directions.
  arXiv  Detail & Related papers  (2020-07-07T14:50:32Z)
• Blockchain-Based Applications in Higher Education: A Systematic Mapping Study [0.0]
  The utilisation of blockchain has moved beyond digital currency to other fields such as health, the Internet of Things, and education. We present a systematic mapping study to collect and analyse relevant research on blockchain technology related to the higher education field.
  arXiv  Detail & Related papers  (2020-06-25T16:24:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.