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<p>Machine learning (ML) is a critical technology that provides pervasive intelligence for the Internet of Things (IoT), enabling smart decision and automation. Meanwhile, Blockchain has emerged as a reliable, secure, decentralized and distributed network with applications in a variety of sectors like healthcare, insurance, finance, banking, and business. The integration of blockchain and ML may further enhance security, optimize data processing and ensure intelligent automation. The linkage of blockchain technology with ML aims to safeguard the privacy of ML models by executing blockchain transparency functions. However, maintaining the integrity of ML models and optimizing blockchain process are challenging. The integration in this work aims to solve challenges like security vulnerability, scalability and computational efficiency. Integration enables automation through smart contracts, enabling secure decision making while preserving data integrity and supporting auditing tasks. Moreover, the security benefits of blockchain networks result from anomaly detection technologies enabled by ML that detects fraudulent activities while defending blockchain networks from security threats. This work presents an organized approach to examine contemporary blockchain-ML research developments, analysis of applications based on the integration of blockchain and ML, technical aspects of Integration and its case studies. Finally, integration with respect to industry focus, followed by open challenges and research problems in ML-based blockchain technology, future directions and emerging trends are discussed in this survey.</p>

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A comprehensive survey on integration of machine learning with secure blockchain-based applications

  • 期刊论文

  • 发表日期：

    2025年9月3日

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  • Chahna Meka,
  • Keerthi Reddy Palakollu,
  • Maria Azees,
  • Arun Sekar Rajasekaran,
  • Ashok Kumar Das,
  • Marko Hölbl

摘要

Machine learning (ML) is a critical technology that provides pervasive intelligence for the Internet of Things (IoT), enabling smart decision and automation. Meanwhile, Blockchain has emerged as a reliable, secure, decentralized and distributed network with applications in a variety of sectors like healthcare, insurance, finance, banking, and business. The integration of blockchain and ML may further enhance security, optimize data processing and ensure intelligent automation. The linkage of blockchain technology with ML aims to safeguard the privacy of ML models by executing blockchain transparency functions. However, maintaining the integrity of ML models and optimizing blockchain process are challenging. The integration in this work aims to solve challenges like security vulnerability, scalability and computational efficiency. Integration enables automation through smart contracts, enabling secure decision making while preserving data integrity and supporting auditing tasks. Moreover, the security benefits of blockchain networks result from anomaly detection technologies enabled by ML that detects fraudulent activities while defending blockchain networks from security threats. This work presents an organized approach to examine contemporary blockchain-ML research developments, analysis of applications based on the integration of blockchain and ML, technical aspects of Integration and its case studies. Finally, integration with respect to industry focus, followed by open challenges and research problems in ML-based blockchain technology, future directions and emerging trends are discussed in this survey.