Blockchain-Enabled Federated Learning Framework for Privacy-Preserving Cyber Threat Intelligence Sharing

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@article{1711894,
      author = {Aidar Imashev},
      title = {Blockchain-Enabled Federated Learning Framework for Privacy-Preserving Cyber Threat Intelligence Sharing},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {5},
      pages = {492-506},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1711894.pdf},
      abstract = {The maturity of cyber threats has enhanced the need for effective cyber threat intelligence (CTI) information exchange between entities. However, traditional centralized sharing services are linked with the challenges of privacy of data, ownership and trust, which discourage cooperation between organizations. In order to address these challenges, this paper will propose a framework of Blockchain-Enabled Federated Learning (BFL) which will ensure that its CTI will be shared in a decentralized and privacy-sensitive fashion. The proposed model exploits the federated learning methodology that enables training models in collaboration between a number of participants without exposing them to local sensitive information, and blockchain technology ensures data integrity, traceability, and trustless coordination with smart contracts. The smart contracts have a mechanism of incentives that promotes fair participation and rewarding of merits. Experimental results indicate that the BFL framework is likely to achieve similar or even superior model accuracy as traditional federated approaches, and far greater privacy protection and scale. The integration of blockchain would also remove the possibility of model poisoning and systems-single point failures, which would ensure clear and transparent sharing of intelligence. The results suggest the potential success of the proposed framework to facilitate secure, trustful, and privacy-conscious cyber defense collaboration among distributed infrastructures.},
      keywords = {Blockchain, Federated Learning, Cyber Threat Intelligence, Privacy Preservation, Smart Contracts, Decentralized Security, Collaborative Defense},
      month = {November},
  }