# EchoIA: A Cloud-Based Implicit Authentication Leveraging User Feedback
## 5 Evaluation
### CPU Utilization and Memory Utilization  
In addition, we evaluated the CPU usage and memory usage for different schemes. In the experiment, we recorded the CPU utilization of different IA schemes at three stages, Start, Sampling, and Authentication. The experiment results are shown in Figure 5a. At the Sampling stage, the CPU utilization of EchoIA is the second-lowest for all five schemes. At the Authentication stage, the CPU utilization of EchoIA is the lowest among all the schemes. Since most of the time the users are at the Authentication stage, the total amount of CPU utilization of EchoIA is the smallest for all the schemes. In the experiment, Multi-Sensor-IA has the highest CPU utilization, but it also has a high authentication accuracy similar to EchoIA.  
Figure 5: CPU Utilization and Memory Utilization (User-End). (**a**) CPU utilization. (**b**) Memory utilization. At the Starting stage, the user-end application and services begin to launch. At the Sampling stage, sensors’ data is sampled and periodically uploaded to the server. The _Initialization_ phase is at the Sampling stage. At the authentication stage, utilizing the model returned from the server, the current user will be classified into two categories, legitimate or illegitimate. The _Authentication_ phase is at this stage. Note that we connected different stages using lines in the figure to improve the readability. The stages are actually independent data points.  
We recorded the memory utilization of various schemes at different stages. The result is shown in Figure 5b, in which the EchoIA has the lowest memory utilization among all the five schemes. Since EchoIA only uses a small portion of features to train the model and to authenticate users, the total amount of memory used to store the data is smaller than other schemes. As shown in Figure 5b, the memory utilization of Multi-Sensor-IA is the highest since it uses all the features and associated sensors' data on the device.  
In the experiment, we evaluated the battery consumption for different schemes. The result is shown in Figure 6. We measure the battery usage of different schemes by calculating the average working hours of battery after fully charged. As shown in Figure 6a, EchoIA has the longest battery lifetime, 23 h on average. The Multi-Sensor-IA has the shortest battery lifetime, four hours on average.  
We also calculated the average battery lifetime of each participant by using EchoIA. Figure 6b shows the battery lifetime derived from the data of seven different participants. They are randomly selected. We calculated an average battery lifetime across all participants' data for comparison purposes, which is 23 h. There are large differences in the battery utilization between users. As shown in the figure, participant 2 has the shortest battery lifetime, which is 16 h. Participant 7, however, has the longest battery lifetime, which is 30 h.  
Figure 6: Battery Utilization. (**a**) Battery utilization of different schemes. M.S. denotes Multi-Sensor-IA. S.S. denotes SilentSense-IA. (**b**) Battery utilization of different participants.