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Cited by 4Network Security and Intrusion DetectionInternet of Things and AIInternet Traffic Analysis and Secure E-voting

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A zero-trust digital twin framework for privacy-preserving multi-dataset intrusion detection in industrial IoT with lightweight blockchain auditing

Open access
Mishra, Shailendra, Aldafas, Tariq Saleh M., Alshammari, Naif S.·2026·Scientific Reports
DOI 10.1038/s41598-026-42041-wPMC PMC13181063
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Abstract

Industrial IoT (IIoT) environments face growing cyber threats due to device heterogeneity and cyber-physical integration. This study proposes a Zero Trust-enhanced intrusion detection framework integrating deep learning anomaly detection, differential privacy, lightweight blockchain-inspired hash-chained ledger and Digital Twin-based situational awareness and visualization of device trust states, designed for low-latency inference suitable for near-real-time IIoT monitoring .A unified dataset was constructed by merging NSL-KDD, CICIDS-2017, and IoT-23 (2,513,419 raw samples unified to 143 features, balanced to 100,000 samples across Normal, DoS, Probe, R2L, U2R classes using SMOTE). Mutual information-based feature selection reduced features to 25. Optimized Multilayer Perceptron (MLP) and CNN–BiLSTM models achieved 89–91% accuracy and 0.89–0.91 macro F1-score, with near-perfect rare-attack detection (F1 ≈ 1.00 for R2L/U2R). Differential privacy (Laplace, ε = 25) reduced accuracy to ~ 78%, quantifying the privacy-utility trade-off. The decoupled Zero-Trust Manager dynamically updates trust scores based on prediction confidence, with tamper-evident SHA-256 hash-chained logging adding negligible latency (~ 1.04–1.06 s for 500 samples). This lightweight, centralized design offers strong cross-domain generalization and deployability for resource-constrained IIoT.

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Crossref · 10.1038/s41598-026-42041-w

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Figures

Fig. 1 Proposed Framework.
Fig. 1 Proposed Framework.CC BY-NC-ND · PubMed Central
Fig. 2 Confusion Matrix MLP.
Fig. 2 Confusion Matrix MLP.CC BY-NC-ND · PubMed Central
Fig. 3 Confusion Matrix CNN–BiLSTM.
Fig. 3 Confusion Matrix CNN–BiLSTM.CC BY-NC-ND · PubMed Central
Fig. 4 ROC Curves per Attack Class.
Fig. 4 ROC Curves per Attack Class.CC BY-NC-ND · PubMed Central
Fig. 5 Device Trust (Blockchain logged).
Fig. 5 Device Trust (Blockchain logged).CC BY-NC-ND · PubMed Central
Fig. 6 Device Trust Scores under Zero-Trust Management.
Fig. 6 Device Trust Scores under Zero-Trust Management.CC BY-NC-ND · PubMed Central
Fig. 7 Device Trust Scores Bar Chart.
Fig. 7 Device Trust Scores Bar Chart.CC BY-NC-ND · PubMed Central
Fig. 8 Model Robustness vs. Poisoning Rate.
Fig. 8 Model Robustness vs. Poisoning Rate.CC BY-NC-ND · PubMed Central
Fig. 9 SHAP Interaction Values between Key Features (F1, F4, F2–) and Other Features.
Fig. 9 SHAP Interaction Values between Key Features (F1, F4, F2–) and Other Features.CC BY-NC-ND · PubMed Central

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Citations

Cites

Show all 3 references

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Metadata

Authors
Mishra, S., Aldafas, T. S. M., & Alshammari, N. S.
Affiliations
Majmaah University (SA)
Corresponding author
  • Mishra, Shailendra
  • Alshammari, Naif S.
ORCID
Alshammari, Naif S.
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2026-03-31
Volume
16
Issue
1
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-026-42041-w
PMC
PMC13181063
OpenAlex
W7147213917
Cited by
4
References
3
Citations by year
2026: 4
FWCI
15.06
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Majmaah University (R-2026-X( number X issued after acceptance))
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
  • Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
Keywords
zero-trust architecture, digital twin, intrusion detection, Industrial Internet of Things, differential privacy, blockchain auditing, privacy-utility trade-off, anomaly detection, multilayer perceptron, CNN-BiLSTM, rare attack detection, trust evaluation, CICIDS2017, NSL-KDD dataset, SMOTE, feature selection, resource-constrained devices

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