Write a PREreview

The increasing integration of cyber-physical components in Industry 4.0 water infrastructures has heightened the risk of false data injection (FDI) attacks, posing critical threats to operational integrity, resource management, and public safety. Traditional detection mechanisms often struggle to generalize across heterogeneous environments or adapt to sophisticated, stealthy threats. To address these challenges, we propose a novel evolutionary transformer-based deep reinforcement learning framework (Evo-Transformer-DRL) designed for robust and adaptive FDI detection in smart water infrastructures. The proposed architecture integrates three powerful paradigms: a transformer encoder for modeling complex temporal dependencies in multivariate time series, a DRL agent for learning optimal decision policies in dynamic environments, and an evolutionary optimizer to fine-tune model hyper-parameters. This synergy enhances detection performance while maintaining adaptability across varying data distributions. Specifically, hyper-parameters of both the transformer and DRL modules are optimized using an improved grey wolf optimizer (IGWO), ensuring a balanced trade-off between detection accuracy and computational efficiency. The model is trained and evaluated on three realistic Industry 4.0 water datasets (secure water treatment (SWAT), water distribution (WADI), and battle of the attack detection algorithms (BATADAL)) which capture diverse attack scenarios in smart treatment and distribution systems. Comparative analysis against state-of-the-art baselines including Transformer, DRL, bidirectional encoder representations from transformers (BERT), convolutional neural network (CNN), long short-term memory (LSTM), and support vector machines (SVM) demonstrates that our proposed Evo-Transformer-DRL framework consistently outperforms others in key metrics such as accuracy, recall, area under the curve (AUC), and execution time. Notably, it achieves a maximum detection accuracy of 99.19%, highlighting its strong generalization capability across different testbeds. These results confirm the suitability of our hybrid framework for real-world Industry 4.0 deployment, where rapid adaptation, scalability, and reliability are paramount for securing critical infrastructure systems.