# Towards Design and Development of Security Assessment Framework for Internet of Medical Things
## 3 Security Assessment of IoMT
### Integrated Fuzzy AHP-TOPSIS Method  
The goal of this study was to create an analytical security framework for the Internet of Medical Things for selecting the most appropriate tactic among several alternatives with the help of integrated AHP and TOPSIS techniques in a fuzzy setting. Fuzzy AHP has been used to calculate assessment preferential weights. Fuzzy TOPSIS is used to decrease the difference among performance improvement values as well as aspiration tiers and choose the appropriate process depending on numerous attributes of six alternative IoMT solutions. Fuzzy AHP: AHP is a procedure for assessing the significance of a range of processes in a problem in order to solve complicated decision-making problems [37; 38; 39; 40]. The AHP method divides a multi-criteria selection challenge into a series of interconnected judgments. Moreover, the true AHP concept seems to have some flaws. The AHP technique is used well in most crisp-information judgment implementations; the AHP method generates and deals with a quite imbalanced scale of judgment [38; 40]. The AHP technique ignores the ambiguity related to the process involved. To address these issues, numerous researchers have combined fuzzy set theory with AHP to reduce uncertainty. To ascertain the weights for summative components, fuzzy AHP is predicated on fuzzy timespan arithmetic with triangular fuzzy numbers (TFNs) as well as confidence indexes with an interval mean [41; 42; 43]. The assessment in this study is conducted with TFNs. The following are the stages in fuzzy AHP:  
Step 1: Assessment hierarchy systems are constructed to identify the most suitable alternative among some of the given alternatives while taking into account the different criteria involved. The constructed hierarchy structure is used to choose the highest-quality alternative. Step 2: Triangular fuzzy numbers are used to calculate the assessment component weights. This study employs TFNs for paired comparisons as well as computes fuzzy weights. The justification for employing a TFN is that it is simple to comprehend and measure for decision makers. Furthermore, modeling TFNs has been shown to be a successful method for articulating issues when the available information is subjective and  
Figure 2: Hierarchy for the security evaluation of IoMT. vague. The following describes the numerical technique for fuzzy AHP. A triplet (a1, a2, a3) can describe a triangular fuzzy number. The membership function \(\mu\ \widetilde{A}\ (x)\) is defined by  
\[\mu\ \widetilde{A}\ (x)=\left\{ \begin{array}{ll}\frac{x-l}{m-l},&l\leq x\leq m, \\ \frac{x-l}{u-m},&m\leq x\leq u,\\ 0,&\text{otherwise}.\end{array} \right\} \tag{1}\]  
Linguistic parameters take on the attributes characterized in their term set: they are a collection of linguistic aspects. Linguistic concepts are subjective classifications of linguistic parameters. A linguistic parameter is one with values that are phrases or sentences from a natural or artificial dialect. This type of interpretation is used here to compare and contrast two standards comparing assessment components utilizing nine basic linguistic phrases, namely, \"Perfect\", \"Absolute\", \"Very good\", \"Fairly good\", \"Good\", \"Preferable\", \"Not Bad\", \"Weak advantage\", and \"Equal\" on a fuzzy nine-tier scale. Every feature set (fuzzified scale) is characterized by three synchronous triangular fuzzy number variables: the left point as well as the middle point. Step 3: Weights are chosen for multiple factors. The steps listed below are involved in determining weights for assessment criteria: (a) Decision makers input their judgment values to create a pair-wise comparative matrix that shows their preference for one criterion over another. Because the attributes are linguistic variables, they are represented by a triplet of triangular fuzzy figures. (b) The geometric mean technique is used to measure the synthesized pair-wise comparison matrix. \(r_{i}\) is characterized as the geometric mean.