Android Malware Characterization Using Metadata and Machine Learning Techniques

Table 4

Full benchmark test with top-15 predictors.

Malware	NumDetectors	F1-score	Precision	Recall
Logistic Regression (train/test)
2%	1	0.82/0.1	0.76/0.06	0.89/0.24
25%	1	0.89/0.57	0.87/0.46	0.91/0.73
50%	1	0.93/0.79	0.94/0.82	0.93/0.77
2%	2	0.8/0.18	0.74/0.12	0.88/0.33
25%	2	0.9/0.68	0.89/0.59	0.9/0.79
50%	2	0.94/0.82	0.95/0.78	0.93/0.86
2%	4	0.81/0.27	0.75/0.19	0.89/0.47
25%	4	0.91/0.73	0.9/0.65	0.91/0.83
50%	4	0.95/0.84	0.97/0.79	0.94/0.89
Support Vector Machine (train/test)
2%	1	0.85/0.08	0.76/0.05	0.96/0.23
25%	1	0.93/0.68	0.92/0.69	0.93/0.67
50%	1	0.96/0.82	0.96/0.87	0.95/0.77
2%	2	0.82/0.16	0.72/0.1	0.95/0.35
25%	2	0.93/0.73	0.93/0.7	0.93/0.76
50%	2	0.96/0.84	0.97/0.9	0.94/0.8
2%	4	0.81/0.26	0.7/0.17	0.97/0.54
25%	4	0.94/0.77	0.94/0.72	0.93/0.83
50%	4	0.96/0.87	0.98/0.89	0.95/0.84
Random Forest (train/test)
2%	1	0.99/0.12	0.99/0.07	0.99/0.33
25%	1	0.99/0.73	0.99/0.7	0.99/0.77
50%	1	0.99/0.84	0.99/0.88	0.99/0.8
2%	2	0.99/0.22	0.99/0.15	0.99/0.46
25%	2	0.99/0.77	0.99/0.73	0.99/0.83
50%	2	0.99/0.87	0.99/0.89	0.99/0.86
2%	4	0.99/0.32	0.99/0.22	0.99/0.59
25%	4	0.99/0.81	0.99/0.76	0.99/0.87
50%	4	0.99/0.89	0.99/0.88	0.99/0.9