From: Machine learning-based dynamic analysis of Android apps with improved code coverage
Classifier | PrecM | RecM | PrecB | RecB | W-FM |
---|---|---|---|---|---|
Random-based approach | |||||
RF | 0.926 | 0.919 | 0.927 | 0.933 | 0.9267 |
MLP | 0.902 | 0.902 | 0.911 | 0.911 | 0.9067 |
SL | 0.897 | 0.899 | 0.908 | 0.906 | 0.9027 |
SMO | 0.88 | 0.912 | 0.917 | 0.887 | 0.8991 |
J48 | 0.89 | 0.894 | 0.903 | 0.9 | 0.8972 |
PART | 0.884 | 0.903 | 0.91 | 0.893 | 0.8834 |
NB | 0.81 | 0.63 | 0.72 | 0.866 | 0.7493 |
State-based approach | |||||
RF | 0.953 | 0.925 | 0.934 | 0.959 | 0.9427 |
MLP | 0.93 | 0.921 | 0.929 | 0.937 | 0.9292 |
SMO | 0.918 | 0.923 | 0.93 | 0.925 | 0.9241 |
J48 | 0.918 | 0.905 | 0.915 | 0.927 | 0.9167 |
PART | 0.91 | 0.916 | 0.923 | 0.917 | 0.9167 |
SL | 0.905 | 0.918 | 0.925 | 0.913 | 0.9157 |
NB | 0.741 | 0.843 | 0.836 | 0.732 | 0.7843 |
Hybrid approach | |||||
RF | 0.948 | 0.911 | 0.922 | 0.954 | 0.9337 |
MLP | 0.937 | 0.911 | 0.921 | 0.944 | 0.9282 |
SMO | 0.921 | 0.92 | 0.928 | 0.928 | 0.9247 |
J48 | 0.92 | 0.912 | 0.921 | 0.928 | 0.9202 |
PART | 0.902 | 0.92 | 0.926 | 0.909 | 0.9147 |
SL | 0.912 | 0.906 | 0.915 | 0.92 | 0.9137 |
NB | 0.746 | 0.814 | 0.816 | 0.748 | 0.7795 |