  In this work, we defined an attack vector for networks utilizing the Internet
of Medical Things (IoMT) devices and compute the probability distribution of
IoMT security threats based on Markov chain and Common Vulnerability Scoring
System (CVSS). IoMT is an emerging technology that improves patients' quality
of life by permitting personalized e-health services without restrictions on
time and site. The IoMT consists of embedded objects, sensors, and actuators
that transmit and receive medical data. These Medical devices are vulnerable to
different types of security threats, and thus, they pose a significant risk to
patient's privacy and safety. Because security is a critical factor for
successfully merging IoMT into pervasive healthcare systems, there is an urgent
need for new security mechanisms to prevent threats on the IoMT edge network.
Toward this direction, the first step is defining an attack vector that an
attacker or unauthorized user can take advantage of to penetrate and tamper
with medical data. In this article, we specify a threat model for the IoMT edge
network. We identify any vulnerabilities or weaknesses within the IoMT network
that allow unauthorized privileges and threats that can utilize these
weaknesses to compromise the IoMT edge network. Finally, we compute the
probability distribution of IoMT threats based on the Markov transition
probability matrix.
