A Novel Evolutionary Optimized Transformer-Deep Reinforcement Learning Framework for False Data Injection Detection in Industry 4.0 Smart Water Infrastructures

Ahmad Salehiyan; Nuria Serrano; F. Hernando-Galego; Diego Martín; José Vicente Álvarez-Bravo

doi:10.32604/cmc.2026.075336

Publication record · 2026

A Novel Evolutionary Optimized Transformer-Deep Reinforcement Learning Framework for False Data Injection Detection in Industry 4.0 Smart Water Infrastructures

Authors: Ahmad Salehiyan · Nuria Serrano · F. Hernando-Galego · Diego Martín · José Vicente Álvarez-Bravo
Year: 2026
Publisher: Tech Science Press
Journal or publication: Computers, Materials & Continua
Google Scholar citations: 2

Journal indicators

CiteScore 2025 (Scopus)6.6

Best quartileQ1

Top 10% in any categoryNo

Positions by category

Modeling and Simulation45/397 · Q1 · percentile 88
Electrical and Electronic Engineering230/1030 · Q1 · percentile 77
Mechanics of Materials98/408 · Q1 · percentile 76
Computer Science Applications269/1022 · Q2 · percentile 73
Biomaterials61/146 · Q2 · percentile 58

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Research summary

An evolutionary Transformer and deep-reinforcement-learning framework detects false-data-injection attacks in Industry 4.0 water infrastructure. Temporal modelling, adaptive decisions and optimized hyperparameters are evaluated across heterogeneous water treatment and distribution testbeds.

Original description prepared for this website; consult the publication for its authoritative abstract.

DOI 10.32604/cmc.2026.075336 ↗ · Open-access version ↗ · View Google Scholar record ↗