# Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT)
## 3 Proposed Model and Research Framework
### Experimental Comparisons
The quantum-enhanced model was trained and tested using the real-world dataset that was provided. The success of the model was evaluated based on how accurately it identified areas of data protection that needed improvement. Standard statistical methods were utilized in order to evaluate the performance of the model. The well-respected quantum machine learning technology developed by IBM allowed the Q-Deep Learning model to execute complex calculations both more quickly and accurately than traditional machine learning approaches. The findings that were collected provided evidence that the method that was recommended was both useful and reliable in detecting security weaknesses in operational Internet of Things (IoMT) infrastructure. These findings illustrate the value of merging quantum computing with deep learning for the purpose of protecting applications used in the healthcare industry. After Q-Deep QC was trained and verified, a set of measures was used to determine the best one. Our deep-learning model was built to make accurate predictions. The confusion matrix and classification model assessment metrics were used to determine whether the traffic on an IoMT network is harmful. Accuracy, precision, recall, and F1-score were also used to evaluate the classification Q-Deep QC models. Some of the measures considered were as follows:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{TN}+ \text{FN}} \tag{8}\]  
\[\text{FPR}=\frac{\text{FP}}{\text{FP}+\text{TN}} \tag{9}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{10}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{11}\]  
\[\text{F1 score}=\frac{2\times\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{12}\]  
Chaos in the Confusion Matrix Model (CMM) is used to determine how well the True Positives (TP), False Positives (FP), False Negatives (FN), and True Negatives (TN) work (TN). The CMM is also used to explain why the results of the classifier model make sense when they are applied to test data whose actual values are known. Table 3 shows how we used the confusion matrix to estimate what might be dangerous and what might be safe to interact with. The ROC curve, which stands for Receiver Operating Characteristic (ROC) curve, was used to show how clinical sensitivity and specificity trade-off against each other. The x-axis shows the number of false positives and the y-axis shows the number of true positives. ROC curve analysis uses measurements such as the Area Under the Curve (AUC) and the Area Between the Curves (ABC). The model can determine the difference between true positives and false positives if the area under the receiver operating characteristic curve (AUC) is greater than or equal to 0.70. \[\text{Sensitivity}=\frac{\text{TP}}{\text{(TP+FN)}} \tag{13}\]  
\[\text{Specificity}=\frac{\text{TN}}{\text{(TN+FP)}} \tag{14}\]  
Experiments show how well different supervised machine-learning classification methods can predict whether IoT network traffic is harmful. So we can make sense of the test results, this is what will be carried out. Four main types of malicious software, namely Distributed Denial of Service (DDoS), Command and Control (C&C), Mirai, and Okiru were predicted for each category. Several different metrics, such as accuracy, precision, recall, and the F1-score, were used to judge the performance of a model. Before using it with a machine learning model, the IoT dataset was divided into thirds: 70% for training, 30% for testing, and 10% for 10-fold cross-validation. In the experiments, different ML classification methods were used, such as RL, BPNN, MLNN, CNN, QNN Q-Deep Learning model, and the proposed model. The IoT dataset was used to obtain the features used in these methods. This research also compares the evaluation criteria for the prediction models of the proposed technique with those of previous studies to predict whether IoT network traffic is malicious or harmless.