# An Adaptive Security Framework for Internet of Things Networks Leveraging SDN and Machine Learning
## 6 Performance Analysis
### Detection Accuracy  
The investigation of detection accuracy demonstrated that the ensemble classifier technique of Enhanced SCAFFOLD allowed for dependable attack detection across a variety of threats and traffic kinds. Nevertheless, depending on the particular network circumstances and attack features, the detection efficiency differed. While more subtle attacks like malware and surveillance probes posed more obstacles, more overt threats like DDoS were detected with extremely high accuracy. The findings reveal areas that might be improved by incorporating external threat information, increasing feature extraction, and adding more training data. Based on its current performance, the framework may not be fully developed or optimized, and more research and development are required to increase its accuracy, recall, and durability. There are a few methods with which to overcome these limitations. First, the ensemble classifier would discover more patterns and behaviors if the training dataset comprised more attack scenarios and network conditions. It would be easy to generalize and identify new dangers. Second, picking the most discriminative features and exploring cutting-edge approaches, like deep learning-based feature learning, may increase the classifier's capacity to identify genuine malicious traffic. Third, integrating external threat intelligence streams and exchanging attack data with other businesses would provide important extra context for model training and threat detection. Further tuning of each ensemble model and researching various techniques or architectures may improve performance. The optimum settings for each model may be found, and overfitting can be decreased with the use of methods like hyperparameter optimization, cross-validation, and model pruning by updating and retraining the models on the most recent assault on a regular basis. Additionally, data are essential to guarantee their continued efficacy as the threats change over time. Adaptive detection skills will be essential to spot new attack patterns in a variety of scenarios as IoT use increases. The Enhanced SCAFFOLD framework may constantly grow and enhance its accuracy, recall, and resilience over time by improving the machine learning models, optimizing feature extraction, using external intelligence, and increasing the training dataset. Continually assessing its performance and refining solutions based on input from the real world are necessary to stay ahead of the always-evolving IoT threat environment to provide a high degree of security. This evaluation provides a baseline to guide the ongoing enhancement of the ensemble classifier's precision, recall, and robustness in its vital detection role within the Enhanced SCAFFOLD framework. Table 10 summarizes the attack detection accuracy of the ensemble classifier under eight different scenarios in an IoT network. The ensemble classifier combines multiple machine-learning models, including random forests, support vector machines, and recurrent neural networks, to leverage their complementary strengths. The true-positive rate refers to the percentage of attack samples that are correctly identified, while the false-positive rate is the percentage of normal samples incorrectly classified as attacks. First examining the performance under background HTTP traffic, the ensemble classifier achieved a strong 0.94 true-positive rate in detecting attacks injected into the HTTP flows. However, the 0.021 false-positive rate shows some normal HTTP packets were \begin{table}
\begin{tabular}{c c c} \hline \hline
**Scenario** & **True-Positive Rate** & **False-Positive Rate** \\ \hline
**Background HTTP Traffic** & 0.94 & 0.021 \\ \hline
**VoIP Calls** & 0.92 & 0.018 \\ \hline
**FTP Sessions** & 0.96 & 0.012 \\ \hline
**Custom IoT Traffic** & 0.93 & 0.024 \\ \hline
**DDoS Attack** & 0.98 & 0.007 \\ \hline
**Reconnaissance** & 0.91 & 0.015 \\ \hline
**MITM Attack** & 0.95 & 0.011 \\ \hline
**Malware Infection** & 0.97 & 0.008 \\ \hline \hline \end{tabular}
\end{table}