## 2 Related Works  
In this section, we examine some studies that deal with the security issues of IoMT network-based devices. In the first study, the authors [8] proposed a new deep learning-based framework incorporating an effective IDS into IoMT systems. First, the feature selection process was carried out by applying a genetic algorithm. Next, the data set was normalized, and finally, a deep learning algorithm was applied to the proposed normalized data set to obtain an effective classification process. The whole-test results proved that the proposed framework performed better than the other ML algorithms in terms of accuracy and F-score. Moreover, this framework can prepare a secure way for data transfer methods in IoMT systems. The authors of [9] proposed a novel IDS system using ML algorithms for detecting IoT network attacks by applying ML-supervised algorithms. First, a Min-Max normalization process and feature selection processes were carried out on the proposed data set, and dimensionality reduction was performed. At the simulation level, the authors used six ML algorithms for the analysis procedure. The simulation results showed that the proposed frameworks and applied ML algorithms achieved sufficient values in terms of accuracy, precision, and F-score evaluation parameters. This study proved that ML techniques can successfully detect anomalies and unexpected attacks using the proposed data set in the IoT environment. In another study, [PERSON], [PERSON] [11] proposed a new deep neural network (DNN)-based framework to develop IDS in the IoMT network, aiming to predict unexpected attacks at the first step and dynamically classify them at the next step in both the network and host side. The feature selection method was used for all of the network parameters. The pre-processed optimization process was carried out on the proposed data set using a genetic algorithm and so in the results it was expected that the execution time would decrease. The authors analyzed the experimental results compared with other ML algorithms. It was confirmed that the proposed framework achieved better results compared to other ML algorithms in terms of accuracy, and time complexity. [PERSON], [PERSON] [12] presented a new hybrid IDS model for the IoMT network focusing on patients' health data analysis gathered from different wearable sensors and predicting unexpected attacks at the edge of the network using a genetic algorithm to satisfy and respond to security and privacy concerns. The experimental results determined the attacks that were occurring through data transmission in the network with higher accuracy and precision over the IoN-IoT data set. In another paper, [PERSON], [PERSON] [13] designed a scalable IDS system to prepare the secure area for the IoMT network using five ML algorithms. In this study, the authors used sensor data gathered from wireless body sensors and other connected medical devices to detect anomalies, attacks, and malicious activities at the device level. The simulation results were extracted from OMNeT that show the proposed IDS system obtained less overhead and a higher accuracy of up to 99.9% using the decision trees algorithm more effectively than the other four ML algorithms. [PERSON], [PERSON] [14] proposed a new model for the IDS system to determine any type of anomalies and attacks such as botnet attacks, DoS/DDoS attacks, and web attacks in the IoMT network by analyzing the CICIDS 2017 data set and applying a deep belief network. The experimental results extracted from MATLAB by applying a deep learning algorithm showed that the proposed method was able to achieve suitable results in terms of accuracy of up to 99.96% in the four above-mentioned different type of attacks. In another piece of research, [PERSON] and [PERSON] [15] presented a new IDS model in the IoMT network for three important steps including detecting, classifying, and predicting unpredictable attacks using a deep learning algorithm and four supervised ML algorithms.