## 2 Related Work
Their comparative analysis of multiple datasets, includingX-IIoTID, Edge-IIoTset, and WUSTL-IIoT-2021, demonstrated the superiority of FDL models over centralized deep learning (CDL) models, providing timely and privacy-preserving intrusion detection. [PERSON] and [PERSON] (2023) [35] examined AI-driven cybersecurity enhancements for industrial control systems, comparing machine learning techniques (KNN, RF) and deep learning architectures (CNN-GRU). Their study achieved exceptionally high accuracy rates--99.99% for KNN and RF and 99.98% for CNN-GRU--on the WUSTL-IIoT-2018 dataset, further confirming the effectiveness of AI-based intrusion detection strategies in IIoT security. Similarly, [PERSON] et al. (2023) [36] proposed a deep learning-based intrusion detection model utilizing feedforward neural networks (FNNs) and convolutional neural networks (CNNs). Their evaluation using the WUSTL-IIoT-2021 dataset demonstrated superior performance, with the FNN-focal model achieving accuracy of 98.95%. [PERSON] et al. (2023) [37] analyzed the impact of manipulated datasets on machine learning models to assess cybersecurity risks in power systems. Based on the WUSTL-IIoT-2021 and WUSTL-IIoT-2018 datasets, their findings revealed that manipulated datasets led to reduced accuracy, increased prediction errors, and longer training times for all algorithms except the boosted tree algorithm. [PERSON] et al. (2023) [38] proposed an IoT intrusion detection system based on machine learning, employing the XGBoost classifier in conjunction with two-stage feature selection methods: binary gray wolf optimization (BGWO) and recursive feature elimination with XGBoost (RFE-XGBoost). Experiments conducted on five publicly available datasets demonstrated the approach's superior accuracy, recall, precision, and F1 score performance. However, the study also highlighted challenges in scaling the method to large datasets due to computational complexity, memory constraints, efficiency, generalization ability, and robustness. [PERSON] et al. (2024) [39] introduced the trees bootstrap aggregation (TBA) algorithm for detecting and classifying IoT-SCADA network traffic, focusing on the IEC-104 network communication protocol. Their study demonstrated TBA's high precision in identifying different network traffic types while reducing false-acceptance rates in heterogeneous IIoT sensor data. [PERSON] et al. (2023) [40] addressed IIoT security concerns by proposing an anomaly-based IDS for detecting zero-day attacks. Their approach utilized semi-supervised learning on large-scale, unlabeled IIoT network traffic, implementing the DeepSAD model in a federated learning framework. While the centralized model outperformed the federated approach in detecting DoS attacks, variations in client performance within the federated setting indicated potential areas for future optimization. [PERSON] et al. (2023) [41] made a notable contribution to IIoT cybersecurity by integrating particle swarm optimization (PSO) and the bat algorithm (BA) for feature selection, significantly improving the efficiency of IIoT-based traffic classification. Their study, which utilized the WUSTL-IIoT dataset, achieved accuracy of 99.99% and precision of 99.96%, substantially enhancing computational efficiency. [PERSON] et al. (2024) [33] systematically evaluated machine learning models, investigating preprocessing techniques and dataset imbalances for IDSs in IIoT environments. Their study demonstrated that applying the synthetic minority oversampling technique (SMOTE) improved binary classification accuracy, with random forest and decision trees achieving 99.98%. The study also introduced a novel multi-class classification approach using SMOTE, enhancing detection performance for various attack types, with RF, DT, and LR achieving near-perfect accuracy. [PERSON] et al. (2023) [42] proposed an anomaly detection-based IDS framework using a transformer model, significantly advancing IIoT cybersecurity.