# Security Threats and Cryptographic Protocols for Medical Wearables
## 5 Discussion  
With the massive use of wearable health-monitoring systems, security challenges are increasing rapidly. Cybercriminals use malware as their main weapon to carry out security attacks, causing significant damage and loss to IoT users. The main objectives of cybercrimes against WHMS are firmware vulnerabilities, communication protocols and applications and sensors to get users' sensitive information or to damage devices. The analysis of the vulnerabilities of a device has the purpose of mitigating or reducing the risks to which the system is exposed, with the purpose to protect the information that is stored in the servers or transmitted through different routing protocols to the network. For this reason, cybersecurity studies for different environments are becoming more and more frequent, as is the case of medical devices, made up of microsensors whose purpose is to monitor different physical phenomenon. In this study, the physical phenomena is the temperature that is measured in diabetic foot. In this study, the spread of malware in a medical network is simulated using an agent-based model to improve the cybersecurity mechanisms that can be applied to this type of network. One of the advantages of ABM is that it considers individual characteristics, which is particularly useful in the case of wearable health-monitoring systems because a failure in a component could cause serious health problems. This is not the case of global models, that include general characteristics of the network. Malware spreading depends on the environment; the spread is different in the military, industrial or medical sectors, Internet, Wi-Fi, BLE, or thermal-aware routing protocols, etc. Some studies suggest implementing security tools such as Honeypots for wireless sensor networks, which allow real-time data collection of a malicious attack in a controlled environment. Three different environments were considered for this simulation. In the first one, 10 individuals wear a device made of six sensors, three per foot (considering a medical device to take foot temperature measures). In this case, the simulation showed that the malware was spreading slowly. After 168 h, there were still 45 sensors (corresponding to 7 persons) that were not infected. In the second simulation the number of devices was considerably increased. It considered 15 individuals with 34 sensors each one, i.e., a total amount of 510 sensors distributed in the corresponding regions of interest. The malware spreads rapidly. In 21.6 h, the number of susceptible are equal to the number of exposed sensors, so, these sensors will not spread the malware. Moreover, from the 11 th hour, infected and exposed increase together until the 18 th, where infected decrease and exposed and recovered increase. As dead sensors do not increase, devices will recovered from the malware attack. The recovery begins when infected decrease under recovered and exposed. The third simulation considered a secure network. In this case, the risk of malware attack and devices infected is medium and low, respectively. An equilibrium is reached in this simulation after 160 h. Moreover, the malware spreading is slow, with a maximum of infected nodes at 76 h.  
Most medical devices do not have sufficient resources to provide complex cryptography. Limitations in their computational capacity, energy consumption, capacity of  
Figure 6: Simulation of malware spreading after infecting a secure WHMS network. transmission and reception of information, etc., make traditional cryptographic algorithms not suitable for them. A cryptographic protocol for constrained resources is considered an acceptable solution to secure health devices. The primary choice for the NIST protocol that could be used with all security considerations is the Ascon-80 pq from the Ascon cipher suite. No weakness was found for this protocol of excellent implementation characteristics.