From Network Intrusion Detection to Blockchain-Backed Endpoint Detection and Response: Mapping the Landscape of Decentralized Detection-and-Response Architectures
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Abstract (arXiv)
While the literature on blockchain-assisted intrusion detection and prevention systems (IDS/IPS) for Internet of Things (IoT) and Industrial Internet of Things (IIoT) networks is mature, existing systematic reviews suffer from two critical limitations: they overlook the structural shift toward modern Endpoint Detection and Response (EDR) and Extended Detection and Response (XDR) architectures, and they conflate blockchain's distinct functional roles into a single monolithic category. This Systematization of Knowledge (SoK) addresses these gaps by proposing a three-axis taxonomy that classifies proposals by detection-system class (NIDS, HIDS, EDR/XDR), blockchain functional role, and response-automation maturity. Synthesizing research published in high-impact venues between 2019 and 2026, we provide a rigorous gap analysis exposing why a genuine per-endpoint blockchain-anchored response loop remains nearly nonexistent due to latency, deployment, and community mismatches. Furthermore, we evaluate structural, cross-cutting challenges persisting across the literature, including consensus latency on constrained devices, post-quantum cryptographic vulnerability, smart-contract attack surfaces, and the adversarial vulnerability of evolving LLM-based detection engines. Finally, we outline a comprehensive research agenda centered on hybrid on-chain/off-chain orchestration to bridge the gap between decentralized trust and rapid response automation.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 67 references that are in the map. First posted on arXiv 1 Oct 2026.
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17 of 67 references.
References · 17
- 2021Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challengescited by 75
- 2021Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocationcited by 55
- 2024Blockchain for Large Language Model Security and Safety: A Holistic Surveycited by 8
- 2023BC4LLM: Trusted Artificial Intelligence When Blockchain Meets Large Language Modelscited by 4
- 2021On Blockchain Architectures for Trust-Based Collaborative Intrusion Detectioncited by 3
- 2022Blockchain-based Monitoring for Poison Attack Detection in Decentralized Federated Learningcited by 2
- 2025Federated Learning-Enhanced Blockchain Framework for Privacy-Preserving Intrusion Detection in Industrial IoTcited by 2
- 2025DoS Attacks and Defense Technologies in Blockchain Systems: A Hierarchical Analysiscited by 2
- 2024Collaborative Cybersecurity Using Blockchain: A Surveycited by 1
- 2024A Survey and Comparison of Post-quantum and Quantum Blockchainscited by 1
- 2024Automated Cybersecurity Compliance and Threat Response Using AI, Blockchain & Smart Contractscited by 1
- 2024CyberNFTs: Conceptualizing a decentralized and reward-driven intrusion detection system with MLcited by 1
5 more
- 2025Blockchain Security Risk Assessment in Quantum Era, Migration Strategies and Proactive Defensecited by 1
- 2026Zer0n: An AI-Assisted Vulnerability Discovery and Blockchain-Backed Integrity Frameworkcited by 1
- 2026Tri-LLM Cooperative Federated Zero-Shot Intrusion Detection with Semantic Disagreement and Trust-Aware Aggregationcited by 1
- 2026Empowering IoT Security: On-Device Intrusion Detection in Resource Constrained Devicescited by 1
- 2026SoK: Federated Learning for Intrusion Detection in Vehicular Networkscited by 1
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