## 4 Experimental Results
### Experimental Results and Analysis  
First of all, this paper tests the impact of environmental attributes on risk assessment. For virtual machine \(M_{2}\), we constructed a large number of normal requests. The experiment is set to send 100 requests per second, operate the virtual machine at 500 s, and manually start 5 computation processes, which consume CPU resources to test the relationship between system CPU consumption and risk assessment. We sample the test results. The CPU monitoring data comes from Nagios. The test results are shown in Figure 4.  
Figure 3: Risk access control model based on XACML. From Figure 4, we can see that the normal range of CPU consumption of virtual machine \(M_{2}\) is between 15% and 30%, and the normal value of risk assessment is 0.4; that is, the risk level is low. The CPU consumption of virtual machine \(M_{2}\) increases to 75% at 500 s, and the corresponding risk assessment value also increases to 0.85. The system is in a state of high-risk level and extremely insecure resources, indicating that the risk assessment algorithm proposed in this paper can accurately assesses risks and reflects the real situation. In the second experiment, we increased the network packet loss rate, combined with CPU utilization, to comprehensively test the impact on the risk value. We set the network packet loss rate in the virtual machine network card. The result is shown in Figure 5. From the figure, we can see that the risk value fluctuates with the fluctuation of CPU utilization and the rate of network packet loss and can respond quickly to them. This is because the proposed quantization method can be calculated in polynomial time, and the proportion of network bandwidth occupied by the transmission of the results is small, so the risk assessment model proposed in this paper is efficient. Next, we conduct a third experiment to test the percentage of rejected requests and the response time of the virtual machines \(M_{1}\) and \(M_{2}\) in the presence of abnormal requests. The experiment is also set to 100 requests per second. At the 100 th second, virtual machines \(M_{1}\) and \(M_{2}\) are operated, and 5 computing processes are manually started to make the CPU resource consumption reach 60%. At the 300 th second, 10 calculations are started. Figure 4: The relationship between the risk value and CPU utilization of virtual machine \(M_{2}\). Figure 5: The relationship between the risk value of virtual machine \(M_{2}\) and the attack. process, so that the CPU resource consumption of the two virtual machines reaches 90%, and an unauthorized access request is added at the 500 th second. The resulting percentage of rejected requests is shown in Figure 6.  
As can be seen from Figure 6, between 0 and 300 s, the percentage of rejected requests of the virtual machines \(M_{1}\) and \(M_{2}\) is not much different, almost coincident, and most requests can be processed normally, and the access success ratio is maintained at above 95. At the 100 th second, which is zone A in Figure 6, the CPU utilization of both virtual machines reaches 60%, and the percentage ratio of rejected requests of virtual machine \(M_{1}\) rises to 13%. From the first experiment, we can get this because the high CPU utilization affects the risk assessment value of the request, so virtual machine \(M_{2}\) will reject some requests with high-risk value, and ordinary access control will not reject legitimate access requests because it does not consider the current system environment, so the percentage of rejected requests for \(M_{1}\) remains unchanged. At the 300 th second, that is, zone B in Figure 6, the CPU utilization of both virtual machines reaches 90%. Because ordinary access control will receive and process requests as long as the requests are legal, the rejection ratio of virtual machine \(M_{1}\) is maintained unchanged, but the percentage of rejected requests of virtual machine \(M_{2}\) has risen to 34%. This is because a risk assessment has been added to the virtual machine \(M_{2}\).