# A Systematic Review of Radio Frequency Threats in IoMT
## 3 Results and Discussion
### Radiofrequency Machine Learning and Data Set Creation  
ML and DL techniques are analyzed in a paper [44] on network-centric intrusion detection systems (IDS). This paper provides an overview of publicly available cyberscurity intrusion detection data sets. Due to inconsistent support categories, there may be insufficient data volume to address research objectives.  
In this paper [45], the author examines endpoint detection and response (EDR). They then demonstrated how data-driven technologies are replacing traditional approaches. Between 1990 and 2019, they studied IEEE, Science Direct, ACM, and Springer Link databases. Alternative methodologies are available for endpoint detection and response (EDR). The research aims to analyze the publication trends in EDR and techniques used for EDR. However, they do not address how ML and DL can be used for intelligent systems. Each of the four categories of machine learning algorithms is represented in cybersecurity management systems (supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning). Using design science principles, each category represents a unique set of machine learning algorithms. EDR technologies such as fire eye endpoint security [46], carbon black response [47], Symantec endpoint protection, Webroot endpoint protection, etc., can be improved through optimized data-driven cybersecurity processes.  
Future research will need to examine how these ML techniques are used with analytics and tools for advanced prediction or intelligent sniffing systems. No evidence or reason was provided for their choice of four databases. The cybersecurity core systems (governance, risk management, information security control, compliance, audit, security program management, operation, information security core module, strategic planning, finance, procurement, innovation, and vendor ecosystem) are a top hierarchy. Additionally, their associated subsystems (compliance management, guidelines, program management, operation management, access control, physical security, network security, endpoint protection, application security, encryption technologies, virtualization, cloud computing, transformative technologies, strategic planning, designing, developing, and maintaining information security program, awareness, education) are categorized as middle-level categories.  
Dimensions and facts include security metrics and measurable quantities. The dimensions against those facts are viewed by region, time, incidents, threats, vulnerabilities, assets, and attacks. Frameworks are developed with multilayer architectures (database, business, presentation, and innovation). Cardinalities from the azure cloud synapse and data brick [48] connect dimensions and facts in the database layer. The business layer implements business logic and security logic, including embedded and available filters. The presentation layer, query items (columns), and query subjects (table) are reflected as functional requirements, non-functional requirements, policies, and data governance. Dimensions such as time, date, and asset are critical, as well as malware infection facts, threats, vulnerabilities, configurations, mitigations, protocol, transmission power, and reception power.  
Our research identifies gaps, develops frameworks and prototypes, and validates them through experiments and analysis. RFML provides insight into how deep learning technologies could be used for identifying modulation and spectrum information and their signal classification.