# Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT)
## 3 Proposed Model and Research Framework
### Experimental Comparisons
Python was used to build the machine learning model and to put data mining strategies into action. IoT traffic can be both bad and good. Four measurements were used to determine the performance score: accuracy, precision, recall, and the F1 score. Table 4 information is visualised in Figure 6. The proposed model performed well as shown in Figure 6. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1** & **Accuracy** \\ \hline RL & 0.7943 & 0.73431 & 0.73432 & 0.81323 \\ BPNN & 0.80235 & 0.75345 & 0.73456 & 0.82344 \\ MLNN & 0.8278 & 0.7767 & 0.7473 & 0.86765 \\ CNN & 0.8454 & 0.8334 & 0.8123 & 0.87347 \\ Q-Deep Learning model & 0.9145 & 0.8934 & 0.7934 & 0.92234 \\ Proposed Model & 0.9934 & 0.90345 & 0.80345 & 0.9934 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of several ML algorithms. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Confusion Matrix** & **Predicted Class (Benign)** & **Predicted Class (Malicious)** \\ \hline Actual Class (Benign) & TP & FN \\ Actual Class (Malicious) & FP & TN \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix. The precision of each experimental model is also compared in Figure 7 with the proposed model outperforming the other models. Vulnerability prediction in IoMT-based application in Figure 8 shows a comparison of the machine-learning algorithms based on the behavior of IoT devices using an existing dataset. Quantum machine learning (QML) exhibits promising results when evaluating the security of IoMT devices when compared to more typical machine learning techniques. Figure 8: Vulnerability prediction in IoMT-based application. Figure 6: Comparative analysis different machine learning model. Figure 7: Comparative analysis of different machine learning models in terms of precision. Quantum computing makes possible the rapid processing and analysis of massive IoMT datasets, which QML leverages to substantially cut down the amount of time spent on challenging computations and pattern discovery. Using typical approaches to machine learning could be proven challenging due to the vast dimensionality, interconnection, and highly sensitive nature of the data in an IoMT environment. On the other hand, the fact that QML is able to manage high-dimensional data fields makes it an effective instrument for locating security vulnerabilities in IoMT systems. In addition to this, it is capable of doing many calculations concurrently, which significantly accelerates the process of vulnerability assessment. QML has the ability to increase prediction and generalization as a result of its status as a probabilistic language that is capable of modeling complex quantum states. This increases the accuracy with which vulnerabilities are found and categorized, which in turn contributes to an improvement in the dependability of IoMT security. The quantum advantage has taken on new significance in light of the increasing intricacy of cyberattacks and the critical importance of maintaining data privacy in the medical industry. We might be able to entirely rethink the way in which we evaluate the safety of IoMT systems by utilizing quantum computing, which would contribute to the systems becoming more reliable and secure.