## 1 Introduction
Further refinement has been achieved through optimization techniques such as the satin bowerbird optimization algorithm, which improves data preprocessing for enhanced model compatibility [17]. Recent developments also incorporate federated learning to enhance privacy while maintaining detection performance. [PERSON] et al. [18] introduced a federated learning-based IDS model that integrates CNNs with attention mechanisms, effectively addressing privacy concerns and accuracy challenges in IIoT environments. This approach is particularly relevant given the increasing emphasis on secure industrial networks. Additionally, ensemble methods have demonstrated significant potential in improving predictive performance. [PERSON] et al. [19] proposed a CNN-LSTM hybrid model that achieved 99% accuracy on the KDD-Cup dataset, reinforcing the effectiveness of ensemble techniques in intrusion detection. [PERSON] et al. [20] further highlighted the importance of hybrid classifiers in detecting sophisticated cyberthreats in secure industrial environments. This study employed the WUSTL-IIoT-2021 dataset to detect cyberthreats in IIoT systems, providing a robust platform for evaluating security mechanisms by simulating real-world industrial conditions [21]. As machine learning continues to demonstrate significant promise in cybersecurity, research in this area has expanded rapidly [22; 23]  
In this study, SCADA network traffic was analyzed using five machine learning models (CART, decision tree, logistic regression, naive Bayes, random forest), five deep learning models (CNN, GRU, LSTM, RNN, MLP), and two hybrid models (CNN-LSTM, LSTM-CNN). A key contribution of this research is the comparative evaluation of machine learning, deep learning, and hybrid models within a unified experimental framework, providing a comprehensive analysis of their relative effectiveness. By systematically applying different hyperparameter configurations, the study aimed to refine model performance and establish a foundation for future research. The model demonstrating the highest accuracy in cyberattack detection was identified and evaluated for potential integration into network-based IDS solutions. When deployed in IDS environments, the proposed model offers superior detection accuracy and reduced error rates compared to conventional security mechanisms. The results indicated that the multilayer perceptron (MLP) model outperformed other approaches, achieving an accuracy of 99.99%, surpassing similar studies in the literature. Contrary to widely held assumptions that hybrid models yield the highest performance, this study demonstrates that standalone models can achieve superior accuracy when applied to the WUSTL-IIoT-2021 dataset. These findings provide valuable insights for advancing cyberattack detection methodologies in IIoT environments. Furthermore, this study makes significant contributions to the cybersecurity literature by providing a detailed analysis of common attack types in SCADA systems, offering a valuable resource for researchers and developers working in this domain. Using a dataset obtained directly from real IIoT devices ensures the practical applicability of the proposed model across both SCADA and IIoT environments. Unlike traditional attack detection techniques, integrating artificial intelligence-based methodologies introduces an innovative perspective, with the effectiveness of the proposed approach validated through comparative analysis with existing studies. In addition to evaluating machine learning and deep learning models, the study also explores hybrid architectures, presenting new insights for cybersecurity experts. The study lays a solid foundation for future research by testing and optimizing various hyperparameters. A comprehensive assessment of SCADA security vulnerabilities and cyberthreats is conducted, enhancing awareness of potential risks. Moreover, advanced big data analytics in this research contribute significantly to data processing methodologies and cyberthreat detection.