# Challenges and Opportunities for Conducting Dynamic Risk Assessments in Medical IoT
## 4 Challenges in Performing Risk Assessment in MIoT
### Similar RA Approaches Specific to IoT  
[PERSON] et al. (2018) [96] proposed a risk analysis methodology tailored to end-to-end systems considering the whole data life-cycle of IoT. It accounts for both static and dynamic features of IoT-based systems to tackle risks throughout layers of data flow. The authors suggest five steps to follow: (i) Identify and model a threat as an attack tree, listing the possible attack vectors towards the threat realisation and identifying the principal vulnerabilities (as leaves); (ii) Map to each vulnerability a qualitative exploitability level translated to a quantitative numerical value within (0:10); (iii) Generate a graph highlighting all dependencies among the identified vulnerabilities; (iv) Compute an exploitability value for all the edges of the graph; (v) Account for dependencies by updating the model following iterative formulas. When considering a dynamic risk approach, however, the methodology lacks a support tool to devise the attack trees and graphs and all the required updates. The mechanism provided is interesting in mapping potential vulnerabilities over IoT assets and converting qualitative scales to quantitative values. [PERSON] and [PERSON] (2012) [97] considered autonomous IoT that requires a risk-based adaptive assessment framework for risk analysis that can sustain predictions for impending issues regarding assets, impact prediction, implementing planned actions for mitigation and reducing risk exposure [41]. They proposed the adaptation of two models for predicting uncertainty, namely, Cyber Value at Risk (CVR) [98] and MicroMort [99], and a mechanism to compute the economic impact of IoT risks. The authors proposed quantifying uncertainties in IoT domains, identifying the most likely attack vectors and combining risk approaches. The manual alternative does not account for any dynamics and changes in attack surface as multiple IoT participate in networks. [PERSON] et al. (2019) [100] suggest using a certification methodology that combines security risk assessment with security testing to certify devices across application contexts. The proposed approach is derived from ETSI (based on ISO 31000 and ISO 29119) and extended to include labelling activities to address certification and tackle security risks related to IoT domains. The work calculates base scores for each identified vulnerability employing a common vulnerability scoring system (CVSS) formula. It proposes an approach to quantifying risks in IoT environments by integrating different standards with known scoring systems; however, it does not address any system dynamics or changes in the attack surface over time, which represent fundamental characteristics of these systems. Formal approaches for modelling and assessing IoT security have been proposed, for instance, by [PERSON] et al. (2017) [101], who combined a hierarchical attack representation model (HARM) to evaluate it using the known symbolic hierarchical automated reliability and performance evaluator (SHARPE). In contrast, [PERSON] et al. (2017) [85] devised IoTRiskAnalyzer to analyse risks using a quantitative probabilistic model-checking approach. These techniques, unfortunately, fall short of the required abstractions to represent complex MIoT, as they must analyse a massive state space when modelling. These shortcomings are alleviated by probabilistic model checking that sustains partial state space analysis; however, these problems will still be present in over-scaled MIoT/IoT networks. Finally, we mention the work of [PERSON] et al. (2021) [102] who implemented an end-to-end assessment framework for IoT, consisting of a vulnerability assessment model equipped with visualisation using AI/ML to process vulnerability descriptions and predict severity scores.