## 6 Conclusions and Future Works  
Integrating SCADA systems with IIoT has significantly increased operational efficiency in various industrial sectors. However, this connection has also introduced numerous vulnerabilities related to DoS attacks and IDS. Understanding these threats and their mitigation mechanisms is crucial to maintaining the integrity and reliability of industrial operations. SCADA systems are critical for monitoring and controlling industrial processes, but their increased exposure to external networks has made them susceptible to various cyberthreats.  
This study examined the network traffic of a SCADA system built with IIoT devices, and both normal and attack traffic were analyzed. This analysis, which was conducted using machine learning, deep learning, and hybrid learning models, aims to make a significant contribution considering the limited studies in the literature. Experimental studies were conducted in the Google Colab environment using the Apache Spark big data platform, and the performances of the models were evaluated with metrics such as accuracy, precision, recall, and F1 score. The MLP model achieved 99.99% accuracy, the CART model 99.98% accuracy, and the logistic regression model 99.86% accuracy, outperforming other methods in the literature. In this process, hyperparameter adjustments were performed for deep learning algorithms and parameter optimizations were performed for machine learning algorithms. The MLP model had the highest accuracy rate and was trained for 10 epochs using ReLU and softmax activation functions. It has a dropout rate of 0.5 and consists of three hidden layers containing 64, 128, and 256 units. The Adam algorithm was used for model optimization, and categorical cross-entropy was adopted as the loss function.  
The proposed model successfully detects security threats in the data streams generated by SCADA systems through AI-driven anomaly detection. Additionally, in-depth analyses of information security, authentication mechanisms, and security technologies are presented, offering a comprehensive framework for protecting industrial control systems. The study reinforces security measures in this critical field by addressing SCADA security challenges within the broader IoT ecosystem.  
Future studies can improve model performance by examining hybrid models and applying data-balancing techniques. Class imbalance problems can be addressed by in  
Figure 4: MLP ROC curve.  
vestigating the effects of over- and undersampling methods. In addition, validating the developed models on different IIoT datasets is important to assess their generalizability. Integrating techniques such as hyperparameter optimization and automatic feature selection can increase the reliability and effectiveness of intrusion detection systems.  
This research received no external funding.  
Not applicable.  
The WWSTL-IIOT-2021 dataset was used. Dataset link: [[https://www.cse.wustl.edu/~jain/iot2/index.html](https://www.cse.wustl.edu/~jain/iot2/index.html)]([https://www.cse.wustl.edu/~jain/iot2/index.html](https://www.cse.wustl.edu/~jain/iot2/index.html)) (accessed 15 September 2024).  
The author declares no conflicts of interest.