| malware-classification-on-microsoft-malware-classification-challenge | Ahmadi et al. (2016): ENT, Bytes 1-G, STR, IMG1, IMG2, MD1, MISC, OPC, SEC, REG, DP, API, SYM, MD2 IMG and Opcode N-Grams + Ensemble Learning (XGBoost) | #1 | Accuracy (10-fold): 0.9976Macro F1 (10-fold): 0.9931 |
| malware-classification-on-microsoft-malware-classification-challenge | HYDRA | #2 | Accuracy (10-fold): 0.9975Macro F1 (10-fold): 0.9951 |
| malware-classification-on-microsoft-malware-classification-challenge | Zhang et al. (2016): Total lines of each Section, Operation Code Count, API Usage, Special Symbols Count, Asm File Pixel Intensity Feature, Bytes File Block Size Distribution, Bytes File N-Gram + Ensemble Learning (XGBoost) | #3 | Accuracy (10-fold): 0.9974Macro F1 (10-fold): 0.9938 |
| malware-classification-on-microsoft-malware-classification-challenge | Ahmadi et al. (2016): API feature vector + XGBoost | #8 | Accuracy (10-fold): 0.9868Macro F1 (10-fold): 0.9638 |
| malware-classification-on-microsoft-malware-classification-challenge | Scaled bytes sequence + CNN & Bidirectional LSTM | #10 | Accuracy (10-fold): 0.9814Macro F1 (10-fold): 0.9662 |
| malware-classification-on-microsoft-malware-classification-challenge | Narayanan et al. (2016): PCA features + 1-NN | #14 | Accuracy (10-fold): 0.9660Macro F1 (10-fold): 0.9102 |
| malware-classification-on-microsoft-malware-classification-challenge | Zero Rule Classifier | #16 | Accuracy (10-fold): 0.2707 |
| malware-classification-on-microsoft-malware-classification-challenge | Random Guess Classifier | #17 | Accuracy (10-fold): 0.1755 |