# Health IoT Threats: Survey of Risks and Vulnerabilities
## 4 Results
### Overview of IoT Security and Privacy in Healthcare
#### 4.1.1 The Perception Layer  
The IoT perception layer is a vital element of healthcare IoT systems, enabling connected devices and sensors to capture and transmit data for analysis and decision-making purposes. Since IoT is an integration of multiple different networks, there are compatibility issues [61] that prone the overall IoT to various security risks. The security issues of this layer include those dealing with physical devices as well as data collection. The proliferation of these devices and sensors has led to significant security and privacy risks, as a variety of IoT devices [61; 77], make implementing a single and unified security design burdensome. For example, there are two RFID-specific security vulnerabilities in this layer: uniform coding and conflict collision [61]. The variation in IoT devices may also cause a problem in data aggregation, which, in turn, jeopardizes data integration [61]. This issue is counted as one of the most critical security issues in healthcare [78]. As healthcare IoT systems continue to evolve and expand, it is vital to remain vigilant in addressing the security and privacy risks associated with the IoT perception layer. Collaboration among healthcare professionals, cybersecurity experts, and technology vendors is essential to mitigate these risks and ensure the safety and integrity of patient data and healthcare systems. It is crucial for healthcare organizations to implement security measures and stay informed of the latest security threats to ensure the safety of their patients and their systems. Various types of security and privacy issues have been reported at the perception layer of healthcare IoT systems, including unauthorized access, data breaches, identity theft,data manipulation, and denial-of-service (DoS) attacks [79]. These attacks can have serious consequences, including compromised patient data, system downtime, and financial loss. One of the significant challenges of the IoT perception layer is the vulnerabilities that come with it. Several studies have investigated security and privacy issues and attacks at the IoT perception layer in healthcare IoT systems. A sniffing attack is a common example when an attacker intercepts data as they are transmitted between connected devices, allowing them to read and potentially modify the data. A replay attack involves the attacker intercepting and then resending a message, potentially leading to unintended consequences. A man-in-the-middle attack occurs when an attacker intercepts data between two parties and alters the data, leading to the parties unknowingly communicating with the attacker rather than each other. Finally, a buffer overflow attack occurs when an attacker sends more data than a system's memory can handle, potentially leading to a system crash or other unintended consequences [64]. One study found that some IoT medical devices have inadequate security controls in place, leaving them vulnerable to attack [80]. Therefore, in healthcare applications, probably the most crucial tasks to provide security are making IoT devices resistant to physical/device tampering and preventing physical/device manipulation, as they are the most common attack in this layer [81]. Since many IoT devices may be placed without extra security, attendance, or involvement in patients' environments, they are still subject to severe attacks. This also makes them susceptible to physical tampering, which could allow intruders to query the IoT devices and search the collection devices to exploit and steal data from patients [81]. Therefore, for the IoT healthcare system to maintain security, node authentication is the essential first step in identifying any malicious nodes because of physical attacks. Physical or device tampering can be serious in healthcare since IoT devices in healthcare are designed and programmed to capture, store, and protect patients' healthcare information. Node authentication is the first critical step in identifying malicious nodes due to physical attacks for the IoT of healthcare systems to keep their safety.