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The Internet of Things (IoT) is progressively changing and offers IoT ecosystems integrated network security challenges that require sophisticated security solutions. In this paper, we discuss the hybrid model that combines Federated Learning (FL) with Random Forest (RF) algorithms along with the validation of Blockchain to provide adaptive network security within IoT frameworks. The proposed architecture merges Blockchain’s protection against unauthorized access with the automatic updates and data processing of FL, decentralizing the security measures within the IoT ecosystems while increasing detection accuracy and safeguarding sensitive information. This framework overcomes the constraints imposed by centralized machine learning intrusion detection techniques, providing solutions to real-world IoT security issues.

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Blockchain-Enhanced Federated Learning for Adaptive IoT Network Security

  • 会议论文

  • 发表日期：

    2026年

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  • Yash Prajapati,
  • Ketul Patel,
  • Nidhi Acharya,
  • Nidhi Dubey,
  • Nisarg Patel

摘要

The Internet of Things (IoT) is progressively changing and offers IoT ecosystems integrated network security challenges that require sophisticated security solutions. In this paper, we discuss the hybrid model that combines Federated Learning (FL) with Random Forest (RF) algorithms along with the validation of Blockchain to provide adaptive network security within IoT frameworks. The proposed architecture merges Blockchain’s protection against unauthorized access with the automatic updates and data processing of FL, decentralizing the security measures within the IoT ecosystems while increasing detection accuracy and safeguarding sensitive information. This framework overcomes the constraints imposed by centralized machine learning intrusion detection techniques, providing solutions to real-world IoT security issues.