Content area

Abstract

The exponential growth of sophisticated cyber threats in Internet of Things (IoT) environments has exposed fundamental weaknesses in existing Cyber Threat Intelligence (CTI) platforms, including centralized architectures, trust deficits, privacy vulnerabilities, and single points of failure. To overcome these limitations, this paper proposes BlockIntelChain, a blockchain-based framework for secure, scalable, and collaborative CTI sharing across distributed IoT networks. The system integrates a hybrid consensus mechanism that combines Proof-of-Stake with reputation-based validator selection, supported by a multi-layered privacy framework employing Differential Privacy (DP), Zero-Knowledge Proofs (ZKP), Homomorphic Encryption, and Secure Multi-Party Computation. BlockIntelChain further embeds Federated Learning (FL) to enable distributed model training directly on IoT edge nodes without exposing raw threat telemetry. Comprehensive evaluations on real-world Malware Information Sharing Platform (MISP) datasets show that BlockIntelChain achieves 923 Transactions per Second at 500 nodes with 99.6% consensus success, while maintaining resilience against 51% and Byzantine attacks tolerating up to 33% malicious validators. Privacy analysis confirms an optimized utility–privacy trade-off, with DP (ε = 0.1) preserving 92% data utility and ZKP achieving 94% verification accuracy. The FL-based models outperform centralized baselines, reaching 96.4% accuracy for IoT malware classification, 94.7% for phishing detection, and 95.2% for network anomaly identification. Economic modeling validates sustainability through contributor growth (156 → 1,245 in 12 months) and improved contribution quality (0.73 → 0.92). The proposed framework directly benefits Security Operation Centers and edge-deployed IoT systems by enabling real-time threat intelligence exchange with strong security, privacy, and efficiency. Comparative benchmarking demonstrates BlockIntelChain’s superiority over MISP, ThreatConnect, and IBM X-Force in decentralization, privacy, and cost efficiency, positioning it as a transformative solution for next-generation privacy-aware CTI ecosystems.

Details

1009240
Title
BlockIntelChain: a blockchain-based cyber threat intelligence sharing architecture
Author
Tolah, Alaa 1 

 Computer Science Department, College of Computing and Informatics, Saudi Electronic University, 13316, Riyadh, Saudi Arabia (ROR: https://ror.org/05ndh7v49) (GRID: grid.449598.d) (ISNI: 0000 0004 4659 9645) 
Volume
16
Issue
1
Pages
190
Number of pages
32
Publication year
2026
Publication date
2026
Section
Article
Publisher
Nature Publishing Group
Place of publication
London
Country of publication
United States
Publication subject
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Online publication date
2025-12-05
Milestone dates
2025-11-14 (Registration); 2025-07-12 (Received); 2025-11-14 (Accepted); 2026-01-03 (Version-Of-Record)
Publication history
 
 
   First posting date
05 Dec 2025
ProQuest document ID
3290016107
Document URL
https://www.proquest.com/scholarly-journals/blockintelchain-blockchain-based-cyber-threat/docview/3290016107/se-2?accountid=208611
Copyright
© The Author(s) 2025. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2026-01-04
Database
ProQuest One Academic