## 2 Related Work  
IIoT systems form a complex structure that integrates various devices and technologies, making them vulnerable to cyberattacks. In particular, denial-of-service (DoS) and distributed denial-of-service (DDoS) attacks can seriously affect the operation of IIoT networks, and such attacks have the potential to turn off production systems [24,25]. Many IIoT devices have outdated technology and are equipped with insufficient security measures. This can make it easier for attackers to infiltrate the network [26]. In addition, since IIoT systems usually operate over cloud-based infrastructures, cloud computing systems also become a target for attackers [27,28]. The WUSTL-IIoT-2021 dataset has become a widely recognized benchmark for evaluating intrusion detection systems (IDSs) in IIoT environments. Its consistent adoption across numerous studies enables direct comparisons between different models, algorithms, and methodologies within a standardized experimental framework [29]. For instance, [PERSON] et al. (2022) [30] introduced a deep learning-based IDS for industrial IoT, employing a multi-stage process. Initially, the dataset was randomly partitioned into training and testing subsets, with 75% allocated for training a deep learning classifier over 75 epochs using a batch size of 1000. The remaining 25% was used in the testing phase to evaluate the classifier's generalization capability. To further enhance generalization, a third stage incorporated 10-fold cross-validation. The resulting model, DeepIIoT, demonstrated exceptional performance on the WUSTL-IIoT-2021 dataset, achieving accuracy exceeding 99% with false-positive and false-negative rates of 0.069% and 0.032%, respectively. Notably, DeepIIoT outperformed alternative approaches tackling similar challenges. In 2023, [PERSON] et al. [31] introduced an intrusion detection model emphasizing advanced preprocessing techniques. Their approach integrated feature selection using the Pearson correlation coefficient (PCC) and outlier detection via the isolation forest (IF) algorithm. The PCC was employed as a feature reduction method to improve model convergence, lower computational costs, and enhance training efficiency. The IF algorithm effectively identified outliers within the Bot-IoT and WUSTL-IIoT-2021 datasets, significantly improving model performance, particularly in handling imbalanced datasets. Similarly, [PERSON] and [PERSON] (2024) [32] proposed a hybrid framework for detecting malicious activities in IIoT environments, incorporating minimum description length (MDL) and transfer learning (TL). This framework standardized dimensions and distributions across diverse IIoT datasets, enabling a unified feature representation. A CNN-GRU model was trained on the integrated datasets, with Bayesian optimization (BO) applied for hyperparameter tuning. Experimental results demonstrated the framework's effectiveness in developing a robust deep learning model capable of generalizing across multiple datasets, emphasizing the crucial role of dataset quality in ensuring reliable training and testing outcomes. [PERSON] et al. (2024) [33] developed a machine learning-based intrusion detection system (IDS) for IIoT networks, evaluating six machine learning algorithms: decision tree (DT), random forest (RF), k-nearest neighbor (KNN), support vector machine (SVM), logistic regression (LR), and naive Bayes (NB). All models performed well, with Matthews correlation coefficient (MCC) scores exceeding 99%. [PERSON] (2023) [9] investigated a flow-based IDS for IIoT utilizing classifiers such as RF, LR, DT, and Gaussian naive Bayes (GNB). The study employed a preprocessed dataset and applied recursive feature elimination to refine the feature set to 11 attributes. A 10-fold cross-validation approach ensured robust generalization with minimal accuracy variance. [PERSON] et al. (2023) [34] introduced a federated deep learning (FDL) model for intrusion detection in consumer-centric IoT, achieving accuracy of 0.9997 for DT with an inference time of 0.1517 us.