## 1 Introduction  
Currently, cyber-physical systems (CPS) are famous systems whose architectural paradigm, combined with communication technologies and pervasive sensing, deliver numerous economic and societal advantages. These systems have become essential for complicated infrastructures, such as transportation, healthcare, energy, and the smart grid [1]. They perform with Internet of Things (IoT) instruments that develop massive volumes of data for communication [2]. A CPS is commonly preferred among the recent inventions of computing technology, such as cloud computing, wireless sensor networks (WSNs), medical sensors, and the Internet of Medical Things (IoMT), to achieve advantages in clinical applications such as home patient care and healthcare processes. These applications deliver many advantages and suitable results for better medicines due to the continued monitoring of patients from remote sites [3, 4].  
Occurring or facing security problems in IoMT networks and their systems can generate disorder in the disease diagnosis process, cause a delay in communication between patients and clinical staff, and result in the patients' private information and clinical history data going missing [5]. Because of all of these important issues, it is critical to determine any types of unauthorized attacks and suspected activities in the IoMT systems as early as possible. By using a powerful intrusion detection system (IDS) [6], due to its advantages and benefits, it becomes a little easier and more practical to recognize an attack by analyzing and examining system parameters, values, and various other variables or by catching variations from normal and usual behavior [7]. All of these security problems have a deep and maybe long-lasting effect on the IoMT systems and their clients. Therefore, this issue highlights the need for designing and developing an accurate and strong IDS for the IoMT network and systems [8]. New models and frameworks based on machine learning (ML) algorithms can be used at both the network and host levels of IDS systems. Models and frameworks prepared with ML algorithms can recognize and discover unpredictable activities and classify them as already-detected suspected and abnormal activities [9; 10].  
In this paper, a novel ML-based prediction model with a feature selection approach is presented and explained for effectively identifying intrusions that are malicious in the IoMT environment. Sensitive medical and healthcare information will be protected by the proposed ML-based prediction model against attacks, malware, and suspicious threats. The main contributions of this study are mentioned below:  
* A newly designed and optimized genetic algorithm-based random forest (GA-RF) model was developed to recognize and analyze malicious movements and cyber-attacks in IoMT devices and their environment.
* The hybrid GA-RF algorithm was applied to two real data sets, NSL-KDD [1] and UNSW_2018_IoT_Botnet, to discover and recognize the effect and result of security standards and measure them in a cyber-security scenario.
* A performance evaluation of the proposed GA-RF model was completed and the results were analyzed and compared with other ML algorithms.  
This paper is organized as follows. Section 2 explains the related works. The ML-based proposed model, with a GA-RF algorithm, is explained in Section 3. The experimental results are presented in Section 4. Finally, the conclusion is presented in Section 5.