Table 7.
Detection Comparison of proposed methods with existing methods on Dataset 1.
| Phishing Models | Accuracy (%) | F-Measure | AUC | TP-Rate | FP-Rate | MCC |
|---|---|---|---|---|---|---|
| Aydin and Baykal [35] | 95.39 | 0.938 | 0.936 | - | 0.046 | - |
| Dedakia and Mistry [24] | 94.29 | - | - | - | - | - |
| Mohammad, et al. [29] | 92.18 | - | - | - | - | - |
| Ubing, et al. [36] | 95.40 | 0.947 | - | - | 0.041 | - |
| Ali and Ahmed [14] | 91.13 | - | - | - | - | - |
| Verma and Das [30] | 94.43 | - | - | - | - | - |
| Hadi, et al. [33] | 92.40 | - | - | - | - | - |
| Chiew, et al. [12] | 93.22 | - | - | - | - | - |
| Rahman, et al. [34] (KNN) | 94.00 | - | - | - | 0.049 | - |
| Rahman, et al. [34] (SVM) | 95.00 | - | - | - | 0.039 | - |
| Chandra and Jana [23] | 92.72 | - | - | - | - | - |
| Folorunso, et al. [60] (Stacking) | 95.97 | - | - | - | - | - |
| Folorunso, et al. [60] (Hybrid NBTree) | 94.10 | - | - | - | - | - |
| Al-Ahmadi and Lasloum [61] | 96.65 | 0.965 | - | - | - | - |
| Alsariera, et al. [11] | 96.26 | - | 0.994 | - | 0.050 | - |
| Ali and Malebary [62] | 96.43 | - | - | - | - | - |
| Ferreira, et al. [6] | 87.61 | - | - | - | - | - |
| Vrbančič, et al. [9] | 96.50 | - | - | - | - | - |
| ∗RoF-FT-1 | 96.78 | 0.968 | 0.995 | 0.968 | 0.035 | 0.935 |
| ∗RoF-FT-2 | 96.83 | 0.968 | 0.996 | 0.968 | 0.033 | 0.936 |
| ∗RoF-FT-3 | 96.49 | 0.965 | 0.988 | 0.965 | 0.037 | 0.929 |
| ∗BG-FT-1 | 96.77 | 0.968 | 0.995 | 0.968 | 0.035 | 0.935 |
| ∗BG-FT-2 | 96.57 | 0.966 | 0.995 | 0.966 | 0.036 | 0.930 |
| ∗BG-FT-3 | 96.44 | 0.964 | 0.990 | 0.964 | 0.037 | 0.928 |
| ∗BT-FT-1 | 97.00 | 0.97 | 0.996 | 0.97 | 0.032 | 0.939 |
| ∗BT-FT-2 | 97.19 | 0.972 | 0.995 | 0.972 | 0.031 | 0.943 |
| ∗BT-FT-3 | 96.9 | 0.969 | 0.995 | 0.969 | 0.033 | 0.937 |
∗
Indicates methods proposed in this study.