  As technology advances towards more connected and digital environments,
medical devices are becoming increasingly connected to hospital networks and to
the Internet, which exposes them, and thus the patients using them, to new
cybersecurity threats. Currently, there is a lack of a methodology dedicated to
information security risk assessment for medical devices.
  In this study, we present the Threat identification, ontology-based
Likelihood, severity Decomposition, and Risk integration (TLDR) methodology for
information security risk assessment for medical devices. The TLDR methodology
uses the following steps: (1) identifying the potentially vulnerable components
of medical devices, in this case, four different medical imaging devices
(MIDs); (2) identifying the potential attacks, in this case, 23 potential
attacks on MIDs; (3) mapping the discovered attacks into a known attack
ontology - in this case, the Common Attack Pattern Enumeration and
Classifications (CAPECs); (4) estimating the likelihood of the mapped CAPECs in
the medical domain with the assistance of a panel of senior healthcare
Information Security Experts (ISEs); (5) computing the CAPEC-based likelihood
estimates of each attack; (6) decomposing each attack into several severity
aspects and assigning them weights; (7) assessing the magnitude of the impact
of each of the severity aspects for each attack with the assistance of a panel
of senior Medical Experts (MEs); (8) computing the composite severity
assessments for each attack; and finally, (9) integrating the likelihood and
severity of each attack into its risk, and thus prioritizing it. The details of
steps six to eight are beyond the scope of the current study; in the current
study, we had replaced them by a single step that included asking the panel of
MEs [in this case, radiologists], to assess the overall severity for each
attack and use it as its severity...
