## 7 Proposed Solution
### Results Analysis  
The classification report shows that the model has a precision, recall, and F1-score of 1.0 for both classes, indicating that the model is able to accurately predict both normal and DDoS traffic. The overall accuracy of the model is also very high, at 1.0, indicating that the model can correctly classify all the test data. The precision of a classification model is defined as the number of true positive predictions divided by the total number of positive predictions, and it measures the model's ability to correctly identify positive cases. In this case, a precision of 1.0 indicates that the model did not make any false positive predictions and correctly identified all positive cases. The recall of a classification model is defined as the number of true positive predictions divided by the total number of positive cases, and it measures the model's ability to correctly identify positive cases from the total number of actual positive cases. A recall of 1.0 indicates that the model correctly identified all actual positive cases. The F1-score is the harmonic mean of precision and recall, and it provides a single metric for evaluating the overall performance of the model. In this case, the F1-score of 1.0 indicates that the model can correctly identify both normal and DDoS traffic with high precision and recall. The macro-avg and weighted-avg values in the classification report provide overall measures of precision, recall, and F1-score across both classes, considering the number of samples in each class. In this case, both measures have a value of 1.0, indicating that the model performs well across both classes. The classification report is depicted in Figure 6,while the confusion matrix of ANN detection is illustrated in Figure 7.  
Furthermore, Figure 8 presents the mdel accuracy and loss. The model accuracy presents the proportion of correct predictions among all predictions. The model loss presents the measure of how well the model fits the training data. The goal is to minimize the loss, which is done using an optimization algorithm during the training process. The obtained results show that both values are optimal. these approaches show promise, it is important to also consider their potential drawbacks to ensure that they can be integrated effectively and provide robust security for IoT networks. Among these drawbacks, we cite the device compatibility. Regarding the design of some IoT devices conceived for conventional networks, it causes an inconvenience while integrating SDN paradigm. To overcome such issues, the adoption of specific protocols allowing such adaptation between SDN and IoT devices presents a solution. While SDN management can provide greater security and control over the network, it can also introduce new security risks. For example, if the SDN controllers or switches are compromised, the entire network may be at risk. Additionally, the use of centralized controllers can create a single point of failure, making the network more vulnerable to attacks. It is important to implement strong security measures and protocols to mitigate these risks. Another potential limitation of using SDN management for IoT networks is its scalability. While SDN management can provide greater control and flexibility over the network, it may not be as easily scalable as other network management approaches. This can limit the ability of the network to grow and evolve over time and may require additional resources or restructuring to accommodate future expansion. In summary, while SDN management can provide many benefits for IoT networks in terms of security, control, and flexibility, there are also potential drawbacks and limitations to consider. It is important to carefully evaluate the specific needs and requirements of the network, and to implement strong security measures and protocols to mitigate potential risks.