Evidence map›Paper›PMID 41345445›Full record

ArticleScientific reports2025

BlockIntelChain: a blockchain-based cyber threat intelligence sharing architecture.

Alaa Tolah

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Alaa TolahComputer Science Department, College of Computing and Informatics, Saudi Electronic University, 13316, Riyadh, Saudi Arabia. a.tolah@seu.edu.sa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Blockchain technologyCyber threat intelligence sharingDecentralized securityIoTMachine learning integrationPrivacy-preserving mechanisms

Identifiers

PMID41345445
PMCPMC12764993

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.