# Blockchain and Federated Learning-based Intrusion Detection Approaches for Edge-enabled Industrial IoT Networks: A Survey Content type: Research Paper Summary: This survey explores the integration of blockchain technology and federated learning to enhance intrusion detection systems (IDS) in industrial IoT (IIoT) networks. As industrial systems become increasingly interconnected, they face advanced persistent threats that static security measures cannot easily mitigate. The paper details how blockchain can provide a tamper-proof logging mechanism for IDS results, while federated learning enables collaborative model training across edge devices without Key concepts: ResearchGate, federated learning, intrusion detection, iiot, security, machine learning, edge computing, adversarial attacks
Paperadvanced

Blockchain and Federated Learning-based Intrusion Detection Approaches for Edge-enabled Industrial IoT Networks: A Survey

ResearchGate

Academic PaperJanuary 2023

Abstract

This survey explores the integration of blockchain technology and federated learning to enhance intrusion detection systems (IDS) in industrial IoT (IIoT) networks. As industrial systems become increasingly interconnected, they face advanced persistent threats that static security measures cannot easily mitigate. The paper details how blockchain can provide a tamper-proof logging mechanism for IDS results, while federated learning enables collaborative model training across edge devices without compromising local data privacy. The authors categorize current methodologies, highlighting how this hybrid approach addresses the challenges of data sparsity and security in industrial settings. The study serves as a foundational resource for understanding how decentralized intelligence can augment cybersecurity posture in high-stakes environments, emphasizing the synergy between privacy-preserving machine learning and immutable verification.

Key Findings

  • 1Federated learning allows for local data training, which keeps sensitive industrial intelligence off the public network.
  • 2Blockchain ensures the immutability of IDS attack signatures across distributed industrial nodes.
  • 3The hybrid approach offers superior robustness against adversarial machine learning attacks compared to centralized IDS.
  • 4Real-time edge-level detection is achievable through decentralized model consensus.

Topics

federated learningintrusion detectioniiotsecuritymachine learningedge computingadversarial attacks
Download / View

Citation

ResearchGate (2023). Blockchain and Federated Learning-based Intrusion Detection Approaches for Edge-enabled Industrial IoT Networks: A Survey. Academic Paper. https://www.researchgate.net/publication/374509285_Blockchain_and_Federated_Learning-based_Intrusion_Detection_Approaches_for_Edge-enabled_Industrial_IoT_Networks_A_Survey

BibTeX

@misc{blockchainand2023,
  title = {Blockchain and Federated Learning-based Intrusion Detection Approaches for Edge-enabled Industrial IoT Networks: A Survey},
  author = {ResearchGate},
  year = {2023},
  howpublished = {\url{https://www.researchgate.net/publication/374509285_Blockchain_and_Federated_Learning-based_Intrusion_Detection_Approaches_for_Edge-enabled_Industrial_IoT_Networks_A_Survey}},
}

Knowledge Explorer

Explore Related Concepts

See how Blockchain and Federated Learning-based Intrusion Detection Approaches for Edge-enabled Industrial IoT Networks: A Survey connects to glossary terms, books, and other research in the Knowledge Graph.

Explore Connections