Security of Cloud-Assisted BANs Using Digital Twin

Wireless Sensor Networks (WSNs) that monitor individuals' physical and medical conditions are called Body Area Networks (BANs). This technology uses wireless sensors to monitor vital signs from the human body. In order to overcome processing and storage limitations, the BAN is often connected to the cloud. Wireless Body Area Network (WBAN), however, opens up the possibility of various security threats when using the cloud. The digital twin (DT) can provide users with hundreds of thousands of interpretations of the physical device without interfering with its normal functionality. It provides an accurate depiction of a cyberattack before time, which maximizes efficiency by reflecting and simulating the physical devices and their relationships with the environment. This paper proposes an integrated security framework for cloud-assisted BANs (CBANs), which will use DT to detect cyberattacks. By combining digital and physical models, cyber-physical security (CPS) can be optimized in advance at low risk and cost. The results of the study show that designing and evaluating techniques for the protection of CBAN using digital twin will help security experts to use an optimized solution without physical testing that will also save time and resources.