## 5 Results and Comparison  
The results of this study were compared across traditional machine learning, deep learning, and hybrid learning algorithms. The dataset was split into 70% training and 30% testing. Commonly accepted hyperparameter values were initially assigned to deep learning algorithms, and fine-tuning was performed to achieve the best results. \begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Number of Epochs & 10 \\ Mini-Batch Size & 100 \\ Optimization Algorithm & Adam \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Optimization and training settings. Although the dataset contains six different attack types, a binary classification approach was adopted: attack traffic vs. normal traffic. This method was successful due to the high similarity between feature distributions of different attack types. The Adam optimization algorithm accelerated and enhanced the model's convergence to the global minimum. Adam combines momentum and RMSprop methods, ensuring a balanced and efficient optimization process. Using 42 features from the WUSTL-2021-IIoT dataset, machine learning, deep learning, and hybrid learning models were analyzed. The results are presented in Figure 2, which compares various machine learning and deep learning algorithms regarding accuracy on the WUSTL-IIoT-2021 dataset. MLP achieved the highest accuracy scores among deep learning models and CART and logistic regression among machine learning models, all exceeding 99.87% accuracy. However, the CNN-LSTM hybrid model performed worse than the standalone CNN and LSTM models on the WUSTL-IIoT-2021 dataset. While hybrid methods generally outperform individual deep learning techniques in the literature, standalone models performed better on this dataset, likely due to their unique characteristics. The lowest-performing models were RNN, CNN, and naive Bayes, with accuracy of 92.74%. These models struggled to classify DoS traffic and normal traffic instances accurately. A detailed comparison of classification metrics--accuracy, precision, recall, and F1 score--is presented in Figure 3 and Table 12.  
The results showed that the LSTM-CNN model had the lowest accuracy, at 92.22%, while the MLP model achieved the highest accuracy, at 99.99%. The highest accuracy values are highlighted in bold. RNN and CNN exhibited the lowest success rates among deep  
Figure 3: Comparison results according to precision, recall, and F1 score parameters. Figure 2: Comparison of accuracy. learning models, whereas CART was the best-performing machine learning model. A comparative analysis of all models is visually presented in Figure 3.  
The dataset is better suited for binary classification models. In the dataset, normal traffic is labeled 0, and attack traffic is labeled 1. The most commonly used models for binary classification in the literature include logistic regression, decision tree, random forest, and support vector machine (SVM). Our study examined these models along with additional approaches. While high classification performance was achieved with models suited for binary classification, MLP yielded the highest accuracy. This is likely due to the heterogeneous nature of attack traffic within the dataset. Although the dataset consists of a simple 0-1 classification structure, the feature variations among different attack types increase complexity. As a result, simpler models such as logistic regression and decision tree struggle with classification. For complex datasets like this, more advanced models should be preferred. Therefore, this study explored binary classification models and various machine learning and deep learning models. MLP model performance and hyperparameters: The MLP model achieved the highest accuracy (99.99%) using 10 epochs and a batch size of 100. It consists of three hidden layers with 64, 128, and 256 neurons. The output layer has a single neuron and employs the sigmoid activation function. The model was trained using binary cross-entropy as the loss function and Adam as the optimizer.