Optimized ensemble machine learning model for cyberattack classification in industrial IoT

The results indicate that the proposed Stacked Ensemble_2 model provides a scalable, real-time detection mechanism for securing ICS and IoT environments. By proving its effectiveness on unseen data, this model demonstrates a significant advancement over traditional methods, offering enhanced accuracy and efficiency in detecting sophisticated cyber threats in critical infrastructure sectors.

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Publication Details

Journal
Frontiers in Artificial Intelligence
Published
2026-01-12
DOI
https://doi.org/10.3389/frai.2025.1685376
Primary Topic
Smart Grid Security and Resilience
Type
article
Field-Weighted Citation Impact
0.00

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article

Optimized ensemble machine learning model for cyberattack classification in industrial IoT

Batool Alabdullah
Frontiers in Artificial Intelligence
Smart Grid Security and Resilience
article

Optimized ensemble machine learning model for cyberattack classification in industrial IoT

Batool Alabdullah
article en

Abstract

The results indicate that the proposed Stacked Ensemble_2 model provides a scalable, real-time detection mechanism for securing ICS and IoT environments. By proving its effectiveness on unseen data, this model demonstrates a significant advancement over traditional methods, offering enhanced accuracy and efficiency in detecting sophisticated cyber threats in critical infrastructure sectors.

Frontiers in Artificial IntelligenceVol. 8
King Faisal University (SA)
King Faisal University
Industry, innovation and infrastructure
Openalex Percentile: Top 92%
Smart Grid Security and Resilience
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Optimized ensemble machine learning model for cyberattack classification in industrial IoT — Batool Alabdullah · Frontiers in Artificial Intelligence (2026) | TGRS Research Map | TGRS