Research Article
Using a Subtractive Center Behavioral Model to Detect Malware
Table 14
Comparison of classifiers from different studies.
| Paper | Classifier | DR (%) | FPR (%) | Acc. (%) | Year |
| Firdausi et al. [43] | NB | 58.1 | 12.8 | 65.4 | 2010 | J48 | 90.9 | 3.8 | 93.6 |
| Ye et al. [25] | NB | 63.3 | — | 50.2 | 2010 | SVM | 84.5 | — | 83.4 | J48 | 56.8 | — | 57.3 |
| Islam et al. [16] | SVM | — | 14 | 84.3 | 2013 | RF | — | 10.4 | 87.8 |
| Santos et al. [44] | KNN K = 2 | — | 14 | 90.7 | 2013 | J48 | — | 9 | 91.2 | NB | — | 31 | 79.6 |
| Yousefi-Azar et al. [45] | SVM | 95 | 5.07 | 93.4 | 2018 | RF | 93.2 | 6.82 | 90.1 | KNN | 90 | 10 | 91.2 |
| Proposed method | J48 | 99.9 | 0.2 | 99.8 | 2019 | SLR | 97.4 | 2 | 97.4 | SMO | 93.2 | 6.8 | 93.1 | KNN | 99.6 | 0.8 | 99.62 | NB | 75.6 | 15.3 | 75.62 |
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