# Challenges and Opportunities for Conducting Dynamic Risk Assessments in Medical IoT
## 2 Contextualisation  
### Threat Modelling, Static and Dynamic Risk Analysis  
Recent years have witnessed the ever-increasing adoption of IoT-based devices in a myriad of application contexts [31]. Examples are Industrial IoT (IIoT), smart manufacturing, smart cities [32; 33], energy generation/storage, and healthcare domains [34]. Although these devices offer remote management capabilities and near real-time sensing, when considering cyber security and privacy, they have considerably enlarged the potential attack surface to protect [35]. Integrating RA and threat modelling (TM) is a traditional information security approach to protect such distributed assets. A noteworthy approach is the process for attack simulation and threat analysis (PASTA), a risk-oriented TM framework that assumes security practitioners are risk managers [36]. It is targeted at helping organisations tackle inherent risks in software to devise hardened products to sustain response to cyber-attacks. [PERSON] et al. (2021) [37] applied the approach to IoT ecosystems showcasing its use with DFDs to demonstrate trust boundaries, as well as presenting an abuse-case diagram for a light control system.  
According to the ISO [17], tackling risk is about determining _uncertainty_, whereas for NIST/US [18], it is a holistic approach encompassing organisations, business processes and information systems. DRA is a relatively recent approach to handle change and assess risk continuously, i.e., to update the RA as new evidence (data) emerges in networks and feeds, proactively preparing for malicious incursions as they progress [23; 38; 39]. As previous work has discussed in detail in the recent literature [40; 41], conducting such analysis in IoT is not trivial, a theme we shall cover in Section 4.  
TM is an exercise in assessing opportunities for system abuse by threat actors and creating the means to cope, withstand or mitigate existing vulnerabilities [42; 43; 44]. Threat analysts could perform TM in the early stages and continuously as managers and system administrators incorporate new technologies and devices into the solution. TM has been successfully used in healthcare [45] to promote better countermeasures and mitigations to specific attacks inherent in IoT shortcomings [46]. Among many techniques available to analysts, we highlight attack trees/graphs and data-flow diagrams (DFD) [47] to depict such concerning situations and employ TM to understand how to address and mitigate governing issues, among other approaches.  
Regarding specific RA frameworks tailored to MIoT, we highlight the IoMT security assessment framework (IoMT-SAF) [48]. It involves healthcare stakeholders in risk processes, allowing them to assess the level of security as observed in distributed MIoT devices across the attack surface. The framework identifies security issues, recommendsresponses and creates scenarios for stakeholders using an ontological approach. The IoMT-SAF's process encompasses identifying security properties, such as: (i) medical operation security; (ii) vulnerability type (user, system, hardware); (iii) attack origin (local, remote), attack type (passive, active), attack difficulty (theoretical, difficult, easy, tools available); (iv) security function (detection, prevention, incident response); and (v) medical data threat (interception, interruption, modification, fabrication, replication).