Metric: Macro F1 (10-fold) (higher is better)
| # | Model↕ | Macro F1 (10-fold)▼ | Extra Data | Paper | Date↕ | Code |
|---|---|---|---|---|---|---|
| 1 | HYDRA | 0.9951 | No | - | - | Code |
| 2 | 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) | 0.9938 | No | - | - | Code |
| 3 | 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) | 0.9931 | No | - | - | Code |
| 4 | SEA | 0.9908 | No | Sequential Embedding-based Attentive (SEA) class... | 2023-02-11 | Code |
| 5 | Orthrus | 0.9872 | No | - | - | Code |
| 6 | Opcode-based Shallow CNN | 0.9856 | No | - | - | Code |
| 7 | Hierarchical Convolutional Network | 0.983 | No | - | - | - |
| 8 | Dynamic Time Wrapping + K-NN | 0.9813 | No | - | - | Code |
| 9 | Autoencoders+Residual Network | 0.9719 | No | - | - | - |
| 10 | Scaled bytes sequence + CNN & Bidirectional LSTM | 0.9662 | No | - | - | Code |
| 11 | Ahmadi et al. (2016): API feature vector + XGBoost | 0.9638 | No | - | - | Code |
| 12 | Multiresolution CNN | 0.9636 | No | - | - | Code |
| 13 | CNN+BiLSTM | 0.9605 | No | - | - | - |
| 14 | Hierarchical Attention Network | 0.9468 | No | - | - | - |
| 15 | Gray-scale IMG CNN | 0.94 | No | - | - | Code |
| 16 | Structural entropy CNN | 0.9314 | No | - | - | Code |
| 17 | Narayanan et al. (2016): PCA features + 1-NN | 0.9102 | No | - | - | Code |
| 18 | DeepConv | 0.9071 | No | - | - | - |
| 19 | MalConv | 0.8902 | No | - | - | - |