## 4 Taxonomy of Smart Healthcare Security Solutions
### Communication Attacks
A detailed explanation of each countermeasure is given below. * **MiTM--**These attacks exploit the communication channel between two legitimate users (healthcare components) in order intercept or inject malicious data into the communication channel [95]. To counter this attack, [PERSON] et al. in [97] studied channel-based MiTM attacks on healthcare devices. The authors of [97] adopted self-anomaly detection using access point scans to detect the MiTM attacks. The self-anomaly detection allows Wireless-Fidelity (Wi-Fi) devices to authenticate themselves without needing access points. With this advantage of self-anomaly detection, they achieved a 99% of detection accuracy. Further, [PERSON] et al. in [98] proposed a secure framework to detect the MiTM attack on IoT-based medical devices. Here, they transmit a small-size signature and message authentication code along with the patient data to detect any MiTM attack. They achieved an excellent detection rate with a low false positive rate (3%). * **Eavesdropping--**To tackle eavesdropping, the authors of [100] examined the effect of eavesdropping in multiple input and single output (MISO)-based wireless channels for image transmission. They segmented the image into two parts: an image consisting of important diagnostic information (requiring high level of reliability and confidentiality) and the image's background (requires less security). Then, the authors observed the link quality of both image segments to control and monitor the eavesdropper interception. Further, in [101], the authors discussed the essential benefits of implantable devices that sends patient's critical healthcare data to the hospital staff. However, they emphasize the risk of security attacks, especially eavesdropping attack on implantables that place the patient's life risk [102]. In their study, they revealed a proper trade-off between information rate and eavesdropping while transmitting sensitive healthcare data. Their study helps the implantable manufacturers to design their devices in a more secure way, i.e., with less security-associated vulnerability. * **Replay attack--**Data integrity is one of the significant challenges in the healthcare industry, since the data can be intercepted and forged by attackers. To prevent this type of attack, [PERSON] et al. in [103] proposed a lightweight framework by utilizing different attributes, such as timestamps, unique identifiers, and self-learning, to detect any replay attacks on IoT devices. The authors of [104] presented a reliable and lightweight framework to tackle replay attacks in the smart healthcare system. Their framework uses unique registration identifiers, timestamps, and authentication phases that improve the data rate and security of the medical data. Their results shows that the proposed framework outperforms current baseline works in terms of computational and communication overhead. * **Protocol-based attack--**IoT-based medical devices communicate with each other using IoT and communication-based protocols, such as HTTP, MQTT, and CoAP. However, these protocols are prone to various security threats. To overcome that, several authors have proposed solutions; for example, The authors in [105] presented a proximity-based secure protocol for smart healthcare systems. This offers a seamless balance between security, privacy, and scalability. Their protocol first verifies the users interacting with the healthcare interface using registration identifier, then it is authenticated using digital signatures. The result shows that the proposed protocol offers acceptable computational complexity and communication overhead. Further, [PERSON] et al. in [99] explored the security challenges of wireless body area network in patient healthcare information system. To confront the potential security threats, they proposed a secure and lightweight authentication protocol that securely exchanges the data between sensors and controller.