## 3 Risk Assessment-Based Dynamic Access Model
### Request a Risk Assessment
#### 3.1.4 Risk Assessment Process  
According to the analysis in the previous section, taking the degree of impact on the risk as the standard, first, calculate the weights of the three attributes of the risk: the main attribute \(B_{1}\), the environmental attribute \(B_{2}\) and the resource attribute \(B_{3}\), which are obtained by comparing the importance of each pair, as shown in Table 4.  
We obtain the attribute initial judgment matrix \(EQ\) according to Table 4:  
\[EQ=\left\{\begin{array}{cccc}0.5&0.7&0.8\\ 0.3&0.5&0.6\\ 0.2&0.4&0.5\\ \end{array}\right\}\]  
The fuzzy matrix \(Q\) of the matrix \(EQ\) is calculated according to Equation (4):  
\[Q=\left\{\begin{array}{cccc}0.5&0.65&0.725\\ 0.35&0.5&0.575\\ 0.275&0.425&0.5\\ \end{array}\right\}\]  
The weight vector of the attribute layer of risk calculated by Equation (6) is \(W=\{0.396,0.321,0.283\}^{T}\). The weight vector of the evidence layer is calculated below. Taking the subject attribute as an example, the important relationship between the pieces of evidence is compared in pairs as shown in Table 5.  
Similarly, according to Equations (3) and (4), the fuzzy matrix of the subject attribute evidence is obtained as  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Risk** & \(\mathbf{C_{1}}\) & \(\mathbf{C_{2}}\) & \(\mathbf{C_{3}}\) & \(\sum\mathbf{C_{i}}\) \\ \hline \(C_{1}\) & 0.5 & 0.7 & 0.8 & 2 \\ \(C_{2}\) & 0.3 & 0.5 & 0.6 & 1.4 \\ \(C_{3}\) & 0.2 & 0.4 & 0.5 & 1.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Attribute pairwise comparison result.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Level** & **Risk Description** \\ \hline \(0.8<R\leq 1.0\) & Extreme risk level, data resources are very insecure \\ \(0.6<R\leq 0.8\) & High-risk level, data resources are very insecure \\ \(0.4<R\leq 0.6\) & At risk, data resources are very insecure \\ \(0.2<R\leq 0.4\) & The risk level is lower, data resources are more secure \\ \(0.0<R\leq 0.2\) & Low risk level, safe data resources \\ \hline \hline \end{tabular}
\end{table}
Table 3: Request risk membership level.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(\mathbf{D_{1}}\) & \(\mathbf{D_{2}}\) & \(\mathbf{D_{3}}\) & \(\mathbf{D_{4}}\) & \(\mathbf{D_{5}}\) \\ \hline \(D_{1}\) & 0.5 & 0.4 & 0.5 & 0.3 & 0.6 \\ \(D_{2}\) & 0.6 & 0.5 & 0.6 & 0.4 & 0.7 \\ \(D_{3}\) & 0.5 & 0.4 & 0.5 & 0.3 & 0.6 \\ \(D_{4}\) & 0.7 & 0.6 & 0.7 & 0.5 & 0.9 \\ \(D_{5}\) & 0.4 & 0.3 & 0.7 & 0.1 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Pairwise comparison results of subject attribute evidence.  
\[Q_{C_{1}}=\begin{pmatrix}0.5&0.4375&0.5&0.3625&0.575\\ 0.5625&0.5&0.5625&0.425&0.6375\\ 0.5&0.4375&0.5&0.3625&0.65\\ 0.6375&0.65&0.6375&0.5&0.7125\\ 0.425&0.3625&0.425&0.575&0.5\end{pmatrix}\]  
The weight vector of the subject attribute evidence calculated by Equation (6) is \(W_{C_{1}}=\{0.202,0.185,0.202,0.172,0.239\}^{T}\). Similarly, the weight vectors for computing resource attributes and environmental attributes are \(W_{C_{2}}=\{0.255,0.280,0.225,0.240\}^{T}\) and \(W_{C_{3}}=\{0.486,0.5139\}^{T}\). Finally, the value obtained by the system is calculated by Equation (8) and combined with the weight value to obtain the risk value.