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Privacy and Trust in Blockchain-Federated Intrusion Detection Systems: Taxonomy, Challenges and Perspectives
Review Article | Published: 02 November 2025
Journal of Reliable and Secure Computing Volume 1, Issue 1, 2025: 4-24
Volume 1, Issue 1, Journal of Reliable and Secure Computing
Volume 1, Issue 1, 2025
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Journal of Reliable and Secure Computing, Volume 1, Issue 1, 2025: 4-24

Open Access | Review Article | 02 November 2025
Privacy and Trust in Blockchain-Federated Intrusion Detection Systems: Taxonomy, Challenges and Perspectives
by
Yuan Cao Yuan Cao 1
Chin Soon Ku Chin Soon Ku 2 *
Rahul Kumar Rahul Kumar 3
Arshad Khan Arshad Khan 4
1 College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
2 Department of Computer Science, Universiti Tunku Abdul Rahman, Kampar 31900, Malaysia
3 Department of Mathematics, SSV Post Graduate College, Hapur 245101, India
4 Yoobee Colleges of Creative Innovation, Auckland 1010, New Zealand
* Corresponding Author: Chin Soon Ku, [email protected]
DOI: 10.62762/JRSC.2025.399812
Received: 05 October 2025, Accepted: 31 October 2025, Published: 02 November 2025  
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Abstract
Intrusion Detection Systems (IDS) play a critical role in protecting modern networks, but traditional centralized designs raise serious concerns regarding data privacy, trust, and scalability. Federated Learning (FL) reduces privacy risks through decentralized model training, and blockchain enhances trust by providing immutability and transparency. Combining these technologies creates a promising paradigm for secure and trustworthy IDS. This paper presents a comprehensive survey of blockchain-federated IDS with a particular focus on privacy and trust. The key contribution is a multi-dimensional taxonomy that integrates IDS architectures, FL strategies, blockchain types, and consensus mechanisms, providing a clear and structured view of this emerging field. We categorize threats into data, communication, and model levels, and map representative defense mechanisms to each. We also review applications in vehicular networks, industrial and medical Internet of Things (IoT), and metaverse scenarios. Finally, we highlight key challenges, including non-IID data, lightweight consensus, incentive mechanisms, and poisoning-resilient aggregation, and outline future research directions.
Graphical Abstract
Privacy and Trust in Blockchain-Federated Intrusion Detection Systems: Taxonomy, Challenges and Perspectives
Keywords
intrusion detection systems
federated learning
blockchain
privacy
trust
Data Availability Statement
Not applicable.
Funding
This work was supported without any funding.
Conflicts of Interest
The authors declare no conflicts of interest.
Ethical Approval and Consent to Participate
Not applicable.
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Cao, Y., Ku, C. S., Kumar, R. & Khan, A. (2025). Privacy and Trust in Blockchain-Federated Intrusion Detection Systems: Taxonomy, Challenges and Perspectives. Journal of Reliable and Secure Computing, 1(1), 4–24. https://doi.org/10.62762/JRSC.2025.399812
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TY  - JOUR
AU  - Cao, Yuan
AU  - Ku, Chin Soon
AU  - Kumar, Rahul
AU  - Khan, Arshad
PY  - 2025
DA  - 2025/11/02
TI  - Privacy and Trust in Blockchain-Federated Intrusion Detection Systems: Taxonomy, Challenges and Perspectives
JO  - Journal of Reliable and Secure Computing
T2  - Journal of Reliable and Secure Computing
JF  - Journal of Reliable and Secure Computing
VL  - 1
IS  - 1
SP  - 4
EP  - 24
DO  - 10.62762/JRSC.2025.399812
UR  - https://www.icck.org/article/abs/JRSC.2025.399812
KW  - intrusion detection systems
KW  - federated learning
KW  - blockchain
KW  - privacy
KW  - trust
AB  - Intrusion Detection Systems (IDS) play a critical role in protecting modern networks, but traditional centralized designs raise serious concerns regarding data privacy, trust, and scalability. Federated Learning (FL) reduces privacy risks through decentralized model training, and blockchain enhances trust by providing immutability and transparency. Combining these technologies creates a promising paradigm for secure and trustworthy IDS. This paper presents a comprehensive survey of blockchain-federated IDS with a particular focus on privacy and trust. The key contribution is a multi-dimensional taxonomy that integrates IDS architectures, FL strategies, blockchain types, and consensus mechanisms, providing a clear and structured view of this emerging field. We categorize threats into data, communication, and model levels, and map representative defense mechanisms to each. We also review applications in vehicular networks, industrial and medical Internet of Things (IoT), and metaverse scenarios. Finally, we highlight key challenges, including non-IID data, lightweight consensus, incentive mechanisms, and poisoning-resilient aggregation, and outline future research directions.
SN  - pending
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
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@article{Cao2025Privacy,
  author = {Yuan Cao and Chin Soon Ku and Rahul Kumar and Arshad Khan},
  title = {Privacy and Trust in Blockchain-Federated Intrusion Detection Systems: Taxonomy, Challenges and Perspectives},
  journal = {Journal of Reliable and Secure Computing},
  year = {2025},
  volume = {1},
  number = {1},
  pages = {4-24},
  doi = {10.62762/JRSC.2025.399812},
  url = {https://www.icck.org/article/abs/JRSC.2025.399812},
  abstract = {Intrusion Detection Systems (IDS) play a critical role in protecting modern networks, but traditional centralized designs raise serious concerns regarding data privacy, trust, and scalability. Federated Learning (FL) reduces privacy risks through decentralized model training, and blockchain enhances trust by providing immutability and transparency. Combining these technologies creates a promising paradigm for secure and trustworthy IDS. This paper presents a comprehensive survey of blockchain-federated IDS with a particular focus on privacy and trust. The key contribution is a multi-dimensional taxonomy that integrates IDS architectures, FL strategies, blockchain types, and consensus mechanisms, providing a clear and structured view of this emerging field. We categorize threats into data, communication, and model levels, and map representative defense mechanisms to each. We also review applications in vehicular networks, industrial and medical Internet of Things (IoT), and metaverse scenarios. Finally, we highlight key challenges, including non-IID data, lightweight consensus, incentive mechanisms, and poisoning-resilient aggregation, and outline future research directions.},
  keywords = {intrusion detection systems, federated learning, blockchain, privacy, trust},
  issn = {pending},
  publisher = {Institute of Central Computation and Knowledge}
}
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