Table 6 Comparison of experimental results(Microsoft Malware Classification Challenge).
From: GCSA-ResNet: a deep neural network architecture for Malware detection
Model | accuracy | precision | Recall-mic | Recall-mac | F1 -mic | F1-mac | F1-wei | SPC | FPR | NPV | DE | Time |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
CNN | 90.86 | 90.86 | 90.53 | 85.00 | 90.86 | 87.12 | 90.92 | 98.83 | 1.17 | 98.84 | 97.97 | 1197 |
CNN-SVM | 93.92 | 93.92 | 93.92 | 91.49 | 93.92 | 91.65 | 93.95 | 99.22 | 0.78 | 99.22 | 98.65 | 1263 |
VIT | 88.48 | 88.48 | 88.48 | 86.13 | 88.48 | 85.08 | 88.51 | 98.52 | 1.48 | 98.52 | 97.44 | 3162 |
Resnet50 | 97.53 | 97.53 | 97.53 | 95.91 | 97.53 | 95.97 | 97.53 | 99.68 | 0.32 | 99.68 | 99.45 | 1802 |
SE-Resnet | 98.26 | 98.26 | 98.26 | 98.16 | 98.26 | 98.10 | 98.27 | 99.78 | 0.22 | 99.77 | 99.61 | 2185 |
CBAM-Resnet | 97.80 | 97.81 | 97.81 | 97.68 | 97.81 | 97.63 | 97.80 | 99.72 | 0.28 | 99.72 | 99.51 | 2241 |
GCSA-Resnet | 98.58 | 98.58 | 98.58 | 98.35 | 98.58 | 98.37 | 98.58 | 99.82 | 0.18 | 99.82 | 99.69 | 2413 |