# A Lightweight Multi-Source Fast Android Malware Detection Model
## 4 Experimental Results and Analysis
### Comparison of Different Methods
Our research found that time consumption is mainly related to CPU performance. It is difficult to measure CPU performance by design parameters due to inconsistencies in the CPU process, architecture design, base clock speed, and turbo boost clock speed. Therefore, we used GeekBench5 to measure CPU performance and plotted Figure 9, which includes the CPUs used in our method and [PERSON]'s. It reflects the single-core and multi-core performance differences of different CPUs. The results of Table 3 and Figure 9 together reflect that the multi-core performance of CPUs mainly influences the time consumption of our method. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **AUC** & **ACC** & **F1-Score** & **Average Time Consumption on PC** \\ \hline [PERSON]’s [41] & 95.36\% & 93.67\% & 94.53\% & 0.64 s \\ [PERSON]’s [42] & 94.34\% & 93.00\% & 94.02\% & 0.22 s \\ [PERSON]’s [13] & 97.66\% & 96.35\% & 96.10\% & 0.21 s \\ [PERSON]’s [43] & 98.82\% & 99.92\% & **98.35**\% & 0.35 s \\ [PERSON]’s [44] & 99.06\% & 98.42\% & 98.20\% & - \\ [PERSON]’s [12] & 98.95\% & 98.53\% & 96.72\% & 0.15 s \\ [PERSON]’s [25] & 99.38\% & 98.77\% & 98.12\% & - \\ \hline [PERSON]’s [5] & 97.06\% & 96.75\% & 96.91\% & 0.19 s \\ MSFDroid & **99.52\%** & 97.26\% & 97.89\% & **0.14 s** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of test results of different methods on PC. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Device** & \multicolumn{2}{c}{**CPU**} & \multicolumn{2}{c}{**Time Consumption**} \\ \cline{2-5}  & **Model** & **Performance** & **MSFDroid** & **[PERSON]’s [5]** \\ \hline Galaxy [7] Pro & Exynos 7870 Octa & 448 & 2.33 s & 3.96 s \\ Nexux 6P & Qualcomm Snapdragon 810 & 514 & 1.57 s & 2.20 s \\ Oppo F3 & Mediately MT6750 & 668 & 1.36 s & 1.76 s \\ OnePlus 3 & Qualcomm Snapdragon 820 & 759 & 1.05 s & 1.65 s \\ OnePlus 5T & Qualcomm Snapdragon 835 & 1627 & 0.81 s & 1.03 s \\ Huawei P30 & HiSilicon Kirin 980 & 2419 & 0.37 s & 0.46 s \\ OnePlus 8 Pro & Qualcomm Snapdragon 865 & 3045 & 0.28 s & 0.38 s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of our method with [PERSON]’s in terms of time efficiency. Table 2 and Figure 10 shows the time consumption of our method and Ruitao's on devices with different CPU performances. CPU performance is referenced to GeekBench5's multi-core score results, time consumption is calculated in seconds, and the data are fitted using a power function. To reduce the impact on Android malware detection, we repeat the detection five times for each APK file. Figure 10 reflects that the predicted time consumption of our method is generally lower than Ruitao's on different devices. Figure 10: The relationship between processor performance and time consumption. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Device** & **CPU** & **Arch** & **Extraction Time** & **Prediction Time** & **Total Time** \\ \hline Galaxy J7 Pro & Exynos 7870 Octa & ARMv8 & 476.85 s & 1758.161 s & 2.232 s \\ Nexus 6P & QCOM Snapdragon 710 & ARMv8 & 392.412 s & 1183.588 ms & 1.575 s \\ Oppo F3 & Mediatek MT6750 & ARMv8 & 375.939 ms & 987.057 ms & 1.363 s \\ OnePlus 3 & QCOM Snapdragon 820 & ARMv8 & 257.021 ms & 793.548 ms & 1.051 s \\ OnePlus 5T & QCOM Snapdragon 835 & ARMv8 & 211.628 ms & 699.563 ms & 0.911 s \\ Huawei P30 & HiSilicon Kirin 980 & ARMv8 & 176.942 ms & 195.222 ms & 0.372 s \\ OnePlus 8 Pro & QCOM Snapdragon 865 & ARMv8 & 96.501 ms & 182.123 ms & 0.279 s \\ x64 Server & Intel Xeon Silver 4210 & x86\_64 & 67.515 ms & 89.088 ms & 0.157 s \\ x64 PC & AMD Ryzen7 5800X & x86\_64 & 58.019 ms & 46.429 ms & 0.104 s \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Efficiency of running on different devices. Figure 9: CPU benchmarks  
Meanwhile, we compare the number of model size and accuracy of our method and [PERSON]'s as shown in Figure 11. We use different base models for ensemble learning.