## 4 Results and Discussion  
This section presents the results from the tests conducted using the proposed method. The implementation of the method described in this article was carried out on the TensorFlow platform, utilizing the Keras high-level library and the Python 3.12 programming language. We compare the results from the proposed model with those from existing works in the field to evaluate its effectiveness and performance. To assess the effectiveness and feasibility of the proposed method, the experimental results were compared with those obtained from decision trees, random forests, support vector machines, and K-nearest neighbors. The number of samples chosen for the training and testing subsets after data cleaning and partitioning is presented in Table 5.  
The experimental evaluation of the CNN-based intrusion detection system was conducted using Google Colab's cloud infrastructure for model training and validation, followed by emulated edge deployment testing. Training leveraged Colab's GPU runtime (Tesla T4 or K80 GPU) with TensorFlow 2.8 and PyTorch 1.12 frameworks. The CNN model was trained using the Adam optimizer (learning rate = 0.001) over 10 epochs (batch size = 32). For edge compatibility, the model was optimized via pruning (30% of weight reduction) and INT8 quantization using TensorFlow Lite, reducing its memory footprint to 120 MB. Classical performance metrics (accuracy, precision, recall, and F1 score) were evaluated for both binary and multiclass classification tasks, with detailed results reported for 6-class and 15-class scenarios. Reproducibility was ensured through publicly shared Google Colab notebooks, GitHub-hosted code, and the DNN-EdgeIIoT dataset, which are accessible for \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Total** & **Train** & **Test** \\ \hline Normal & 1,363,998 & 1,091,198 & 272,800 \\ \hline DDoS\_UDP-based attack & 121,567 & 97,254 & 24,313 \\ \hline DDoS\_ICMP-based attack & 67,939 & 54,351 & 13,588 \\ \hline DDoS\_TCP-based attack & 50,062 & 40,050 & 10,012 \\ \hline DDoS\_HTTP-based attack & 48,544 & 38,835 & 9709 \\ \hline SQL\_injection-based attack & 50,826 & 40,661 & 10,165 \\ \hline Password-based attack & 49,933 & 39,946 & 9987 \\ \hline Vulnerability\_scanner-based attack & 50,026 & 40,021 & 10,005 \\ \hline Uploading-based attack & 36,807 & 29,446 & 7361 \\ \hline Backdoor-based attack & 24,026 & 19,221 & 4805 \\ \hline Port\_Scanning-based attack & 19,977 & 15,982 & 3995 \\ \hline XSS-based attack & 15,066 & 12,053 & 3013 \\ \hline Ransomware-based attack & 9689 & 7751 & 1938 \\ \hline MITM-based attack & 358 & 286 & 72 \\ \hline Fingerprinting-based attack & 853 & 682 & 171 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Number of samples selected for training and test subsets. direct integration into Colab workflows. This setup highlights the model's adaptability to cloud-based development and edge deployment, balancing computational efficiency with robust intrusion detection capabilities in IoT/IIoT networks. In evaluating the proposed method, random forest, support vector machine, K-nearest neighbor, and decision tree algorithms were compared as benchmark algorithms. Tables 6-8 show the confusion matrices for the proposed CNN-based method in 2-class, 6-class, and 15-class scenarios, respectively. The confusion matrix is an essential tool for evaluating machine learning model performance, especially for intrusion detection in IoT networks. It enables comparison between the model's predictions and actual labels, providing insight into the model's accuracy and effectiveness. In Table 6, the binary classification results (2-class) indicate an accuracy of 100% and a sensitivity of 100%, meaning that the model has correctly identified all actual attacks and has not misclassified any attacks as normal. Additionally, the model's precision is 100%, reflecting its high ability to accurately predict attacks, and the F1 score is 1.00.