# A Risk Assessment Framework for Mobile Apps in Mobile Cloud Computing Environments
## 3 Results
### Ensemble ML Model
Table 6 presents the results along with the results obtained from applying each of the algorithms of the ensemble  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Algorithm** & **Dataset** & **TP** & **FP** & **TN** & **FN** & **CA** & **ER** & **PR** & **RC** & **FM** & **FPR** & **FNR** & **FAR** \\ \hline \multirow{3}{*}{C1} & Permissions & 3451 & 201 & 1774 & 235 & **92.3** & 7.7 & 94.5 & 93.62 & 94.06 & **10.18** & **6.38** & **8.28** \\  & Intents & 3455 & 1238 & 723 & 235 & **73.93** & 26.07 & 73.62 & 93.63 & 82.43 & **63.13** & **6.37** & **34.75** \\  & Hybrid & 3435 & 153 & 1842 & 231 & **93.22** & 6.78 & 95.74 & 93.7 & 94.71 & **7.67** & **6.3** & **6.99** \\ \hline \multirow{3}{*}{C2} & Permissions & 3520 & 186 & 1789 & 166 & **93.78** & 6.22 & 94.98 & 95.5 & 95.24 & **9.42** & **4.5** & **6.96** \\  & Intents & 3478 & 1248 & 713 & 212 & **74.16** & 25.84 & 73.59 & 94.25 & 82.65 & **63.64** & **5.75** & **34.69** \\  & Hybrid & 3504 & 144 & 1851 & 162 & **94.59** & 5.41 & 96.05 & 95.58 & 95.82 & **7.22** & **4.42** & **5.82** \\ \hline \multirow{3}{*}{C3} & Permissions & 3489 & 296 & 1679 & 197 & **91.29** & 8.71 & 92.18 & 94.66 & 93.4 & **14.99** & **5.34** & **10.17** \\  & Intents & 3428 & 1244 & 717 & 262 & **73.35** & 26.65 & 73.37 & 92.9 & 81.99 & **63.44** & **7.1** & **35.27** \\  & Hybrid & 3480 & 249 & 1746 & 186 & **92.32** & 7.68 & 93.32 & 94.93 & 94.12 & **12.48** & **5.07** & **8.78** \\ \hline \multirow{3}{*}{C4} & Permissions & 1043 & 61 & 1914 & 2643 & **52.23** & 47.77 & 94.47 & 28.3 & 43.55 & **3.09** & **71.7** & **37.4** \\  & Intents & 1042 & 86 & 1875 & 2648 & **51.62** & 48.38 & 92.38 & 28.24 & 43.25 & **4.39** & **71.76** & **38.07** \\  & Hybrid & 1306 & 66 & 1929 & 2360 & **57.15** & 42.85 & 95.19 & 35.62 & 51.85 & **3.31** & **64.38** & **33.84** \\ \hline \multirow{3}{*}{C5} & Permissions & 3543 & 333 & 1642 & 143 & **91.59** & 8.41 & 91.41 & 96.12 & 93.71 & **16.86** & **3.88** & **10.37** \\  & Intent & 3414 & 1251 & 710 & 276 & **72.98** & 27.02 & 73.18 & 92.52 & 81.72 & **63.79** & **7.48** & **35.64** \\  & Hybrid & 3488 & 248 & 1747 & 178 & **92.47** & 7.53 & 93.36 & 95.14 & 94.24 & **12.43** & **4.86** & **8.64** \\ \hline \multirow{3}{*}{C6} & Permissions & 3471 & 202 & 1773 & 215 & **92.63** & 7.37 & 94.5 & 94.17 & 94.33 & **10.23** & **5.83** & 8.03 \\  & Intent & 3463 & 1261 & 700 & 227 & **73.67** & 26.33 & 73.31 & 93.85 & 82.32 & **64.3** & **6.15** & **35.23** \\  & Hybrid & 3461 & 164 & 1831 & 205 & **93.48** & 6.52 & 95.48 & 94.41 & 94.94 & **8.22** & **5.59** & **6.91** \\ \hline \multirow{3}{*}{C7} & Permissions & 3526 & 243 & 1732 & 160 & **92.88** & 7.12 & 93.55 & 95.66 & 94.59 & **12.3** & **4.34** & **8.32** \\  & Intent & 2439 & 610 & 1351 & 1251 & **67.07** & 32.93 & 79.99 & 66.1 & 72.38 & **31.11** & **33.9** & 32.5 \\  & Hybrid & 3472 & 171 & 1824 & 194 & **93.55** & 6.45 & 95.31 & 94.71 & 95.01 & **8.57** & **5.29** & 6.93 \\ \hline \multirow{3}{*}{C8} & Permissions & 3552 & 403 & 1572 & 134 & **90.51** & 9.49 & 89.81 & 96.36 & 92.97 & **20.41** & **3.64** & **12.02** \\  & Intent & 3460 & 1289 & 672 & 230 & **73.12** & 26.88 & 72.86 & 93.77 & 82 & **65.73** & **6.23** & **35.98** \\ \cline{1-1}  & Hybrid & 3504 & 349 & 1646 & 162 & **90.97** & 9.03 & 90.94 & 95.58 & 93.2 & **17.49** & **4.42** & **10.96** \\ \hline \multirow{3}{*}{C9} & Permissions & 3490 & 268 & 1707 & 196 & **91.8** & 8.2 & 92.87 & 94.68 & 93.77 & **13.57** & **5.32** & **9.44** \\ \cline{1-1}  & Intent & 3455 & 1276 & 685 & 235 & **73.26** & 26.74 & 73.03 & 93.63 & 82.06 & **65.07** & **6.37** & **35.72** \\ \cline{1-1}  & Hybrid & 3477 & 218 & 1777 & 189 & **92.81** & 7.19 & 94.84 & 94.47 & **10.93** & **5.16** & **8.04** \\ \hline \multirow{3}{*}{C10} & Permissions & 3512 & 291 & 1684 & 174 & **91.79** & 8.21 & 92.35 & 95.28 & 93.79 & **14.73** & **4.72** & **9.73** \\ \cline{1-1}  & Intent & 3453 & 1273 & 688 & 237 to the same dataset. The comparison shows that the ensemble ML model performed better than the individual classifiers.