Healthcare organisations are constantly facing sophisticated cyberattacks due to the sensitivity and criticality of patient health care information and wide connectivity of medical devices. Such attacks can pose potential disruptions to critical services delivery. There are number of existing works that focus on using Machine Learning (ML) models for predicting vulnerability and exploitation but most of these works focused on parameterized values to predict severity and exploitability. This paper proposes a novel method that uses ontology axioms to define essential concepts related to the overall healthcare ecosystem and to ensure semantic consistency checking among such concepts. The application of ontology enables the formal specification and description of healthcare ecosystem and the key elements used in vulnerability assessment as a set of concepts. Such specification also strengthens the relationships that exist between healthcare-based and vulnerability assessment concepts, in addition to semantic definition and reasoning of the concepts. Our work also makes use of Machine Learning techniques to predict possible security vulnerabilities in health care supply chain services. The paper demonstrates the applicability of our work by using vulnerability datasets to predict the exploitation. The results show that the conceptualization of healthcare sector cybersecurity using an ontological approach provides mechanisms to better understand the correlation between the healthcare sector and the security domain, while the ML algorithms increase the accuracy of the vulnerability exploitability prediction. Our result shows that using Linear Regression, Decision Tree and Random Forest provided a reasonable result for predicting vulnerability exploitability.

In this work, we present an approach for automatic translation of tock-CSP into Timed Automata (TA) for Uppaal to facilitate using Uppaal in reasoning about temporal specifications of tock-CSP models. The process algebra tock-CSP provides textual notations for modelling discrete-time behaviours, with the support of tools for automatic verification. Automatic verification of TA with a graphical notation is supported by Uppaal. The two approaches provide facilities for automatic verification. For instance, liveness requirements are difficult to specify with the constructs of tock-CSP, but they are easy to specify and verify in Uppaal. We have developed a translation technique and a tool based for translating tock-CSP into a network of small TAs for capturing the compositional structure of tock-CSP. For validating the rules, we begin with an experimental approach based on finite approximations to trace sets. Then, we explore using structural induction to establish the correctness.

In this work, we consider translating tock-CSP into Timed Automata for Uppaal to facilitate using Uppaal in reasoning about temporal specifications of tock-CSP models. The process algebra tock-CSP provides textual notations for modelling discrete-time behaviours, with the support of tools for automatic verification. Similarly, automatic verification of Timed Automata (TA) with a graphical notation is supported by the Uppaal real-time verification toolbox Uppaal. The two modelling approaches, TA and tock-CSP, differ in both modelling and verification approaches, temporal logic and refinement, respectively, as well as their provided facilities for automatic verification. For instance, liveness requirements are difficult to specify with the constructs of tock-CSP, but they are easy to specify and verify in Uppaal. To take advantage of temporal logic, we translate tock-CSP into TA for Uppaal; we have developed a translation technique and its supporting tool. We provide rules for translating tock-CSP into a network of small TAs for capturing the compositional structure of tock-CSP that is not available in TA. For validation, we start with an experimental approach based on finite approximations to trace sets. Then, we explore mathematical proof to establish the correctness of the rules for covering infinite traces.