# Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT)
## 2 Background
### Machine Learning Models in IoMT  
In vulnerability prediction in IoMT-based applications, machine learning is used to analyze risks and find security vulnerabilities. It identifies and evaluates threats based on patterns in the network traffic generated by IoMT devices. It uses the features of dynamically updated threats to determine and assess them. The \"vulnerability\" of a system is a flaw that an attacker can take advantage of because it is built into the system. Most of the time, vulnerabilities can be found and possibly used, because they are common knowledge. On the other hand, risks consider not only the environment, configuration, behavior, and security policy but also one or more underlying weaknesses. Some risks can change how serious IoT Security [16] thinks a vulnerability is, but they only appear on the device details page and not on the vulnerability page. Specifically, IoT Security calls a vulnerability \"possible\" when it affects a specific device type, model, and version number. At least one device fits this type, but the model and version number are unknown. A device is vulnerable if it can be broken. If a flaw is only found in devices with a specific serial number, there are devices with unknown serial numbers that fit the description of the flaw; then the flaw is still a possibility. The vulnerability has not yet been used in this situation. The IoT has the potential to simultaneously improve patient care and lower healthcarecosts at the same time. However, most IoT devices can be hacked, raising cybersecurity concerns that could hurt both patient care and business finances [17; 18]. [PERSON] et al. [19] summarized the typical research methods used in IoT security. Every study that finds a hole in the Internet of Things (IoT) infrastructure was carefully examined using a standard research method. Both the problems that needed to be solved and the tasks that needed to be performed were discussed. [PERSON] et al. [20] provided a thorough discussion of the security issues that plagued the Internet of Things (IoT) sector and looked at some of the ways that these issues were dealt with in the past. The authors provided a high-level overview of security in the IoT and then went into more detail about the specific security features built into the most popular IoT communication protocols. Next, the authors reported some of the attacks on real IoT devices in the literature. This was performed to show the importance of building security in IoT systems and the security problems common in commercial IoT solutions. [PERSON] et al. [21] examined how safe MQ Telemetry Transport (MQTT) servers were to see whether vendors and users took safety measures. Their research showed that not all MQTT servers required a password to connect to the network. Their results provided an excellent way to investigate the safety of IoT devices and encouraged the creation of a more secure ecosystem for IoT systems. [PERSON] et al. [22] obtained a lot of information about network activity from a wide range of commercial IoT devices and ran a series of tests to compare different ways of classifying security concerns. Their research showed that (a) the light gradient-boosting machine (LGBM) algorithm provided very accurate detection results, and (b) their flow-based approach was robust and could handle situations in which the other ways of identifying NAT devices could not (such as encrypted, non-TCP, or non-DNS traffics). The LGBM algorithm yielded outstanding results in terms of finding things. Other methods to find NAT-hidden devices had their limitations, but their flow-based method could work in these situations. [PERSON] et al. [23] used machine learning to create a tool called iDetect that can detect security flaws in the C/C++ code of IoT operating systems (ML). With the help of the Software Assurance Reference Dataset (SARD) and the source code of 16 different releases of IoT operating systems, a tagged dataset of vulnerable and safe codes was created.