2021/04/23 by Maha Ali Allouzi, Javed Khan, Allouzi, Maha Ali +2 · 2 citations
Computer Science · Engineering · #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Internet of Things and AI #IoT and Edge/Fog Computing #Wireless Body Area Networks #cs.CR
paper · pdf · doi:10.48550/arxiv.2104.11580
arxiv created 2021/04/23 · openalex publication_date 2021/04/23 · arxiv updated 2021/04/26 · openalex created_date 2021/05/10 · openalex updated_date 2026/07/28
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.