### Problem Statement, Research Aim, and Objectives  
An IoT or any IoMT applications and systems need to ensure the safety and security of the devices, network, and data, especially when dealing with sensitive health and care data. As stated by [6], IoMT devices must incorporate confidentiality, integrity, and authentication within their designs to ensure the privacy of the user data and the accuracy of the data. As the IoMT refers to medical devices, the impact of a security incident could lead to permanent health consequences for patients and loss of life. This is a significant risk that requires great care. It has been generally neglected previously due to the difficulties in achieving a perfectly balanced state of security and system performance requirements. IoT devices are heavily resource-constrained, meaning significant challenges occur when incorporating security into these devices. So, computation and memory constraints are two components that may affect the level of security that can be incorporated into these devicesbecause security features are resource-demanding in nature. So, there is a clear research gap regarding protecting the IoMT system when all the essential security features needed in protecting the data, devices, and network are taken into account, especially when the devices are a resource constraint. Medium Access Control (MAC) spoofing attacks are common when the network does not filter the MAC addresses and port security is not taken into account at the data link layer of switches. A framework for detecting the MAC and IP (Internet Protocol address) using network characteristics is provided in [7], and there are various methods adopted in detecting MAC spoofing attacks including using the signal strength of the attacker's device [8], and channel state information [9]. MAC spoofing can be prevented through port security at the data link layer, as highlighted in [10,11]. However, it is not appropriate to assume that port security is already enabled in every network, so developing a solution to avoid data leakage in the event of a MAC spoofing attack is one of the objectives of this research. DoS and DDoS attacks are some of the most common cyber-attacks to take down network services. A detailed analysis of such DoS and DDoS attacks is given in [12]. An attack like DoS or DDoS can be detected using different methods including machine learning techniques [13] and hidden Markov models [14]. Such attacks in an IoT network can be prevented by using different methods including Blockchain technology, as described in [15,16], but they are resource-demanding solutions in nature. So, it is important to have a solution that is not resource-demanding in the process of avoiding DoS or DDoS attacks, which is one of the objectives of this paper. Moreover, when a security solution is developed, the system should not consider and concentrate only on a few security aspects while omitting other security features, leading to data disclosure to unauthorised users. So, to tackle these issues, security and privacy safeguarding mechanisms should be addressed from a holistic perspective. The proposed model will aim to ensure that the sensitive data of patients are collected securely by the sensors and confidentially transmitted to the server by maintaining data integrity, and that the data stored at the server are safeguarded from any form of unauthorised access. Thus, this paper is curated to aid secure-by-design solutions for smart hospital monitoring or remote home-based monitoring. A security solution is not something that should be incorporated at the end or after the system development process, but rather it should be incorporated from the start of the design of the system and throughout the developmental process to safeguard the system as a whole and so that all the vulnerable points are needed as the system development progresses and it is this approach that is taken for this paper.