| Rank | Model |
Accuracy (10-fold) | LogLoss | Macro F1 (10-fold) | Accuracy (5-fold) | F1 score (5-fold) | Accuracy |
Paper | Code | Year |
| 1 |
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) |
0.9976 | – | 0.9931 | – | – | – |
HYDRA: A multimodal deep learning framework for malware classification
|
danielgibert/mlw_classification_hydra |
2020 |
| 2 |
HYDRA |
0.9975 | – | 0.9951 | – | – | – |
HYDRA: A multimodal deep learning framework for malware classification
|
danielgibert/mlw_classification_hydra |
2020 |
| 3 |
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) |
0.9974 | – | 0.9938 | – | – | – |
HYDRA: A multimodal deep learning framework for malware classification
|
danielgibert/mlw_classification_hydra |
2020 |
| 4 |
Orthrus |
0.9924 | – | 0.9872 | – | – | – |
Orthrus: A Bimodal Learning Architecture for Malware Classification
|
danielgibert/mlw_classification_orthrus |
2020 |
| 5 |
Opcode-based Shallow CNN |
0.9917 | 0.0244 | 0.9856 | – | – | – |
Convolutional Neural Network for Classification of Malware Assembly Code
|
danielgibert/mlw_classification_cnn_assembly |
2017 |
| 6 |
Hierarchical Convolutional Network |
0.9913 | 0.0419 | 0.9830 | – | – | – |
A Hierarchical Convolutional Neural Network for Malware Classification
|
|
2019 |
| 7 |
SEA |
0.9912 | 0.0431 | 0.9908 | – | – | – |
Sequential Embedding-based Attentive (SEA) classifier for malware classification
|
Muhammad4hmed/SEA |
2023 |
| 8 |
Dynamic Time Wrapping + K-NN |
0.9894 | 0.367724 | 0.9813 | – | – | – |
Classification of Malware by Using Structural Entropy on Convolutional Neural Networks
|
danielgibert/mlw_classification_structural_entropy |
2018 |
| 9 |
Ahmadi et al. (2016): API feature vector + XGBoost |
0.9868 | – | 0.9638 | – | – | – |
HYDRA: A multimodal deep learning framework for malware classification
|
danielgibert/mlw_classification_hydra |
2020 |
| 10 |
Autoencoders+Residual Network |
0.9861 | 0.106343 | 0.9719 | – | – | – |
An End-to-End Deep Learning Architecture for Classification of Malware’s Binary Content
|
|
2018 |
| 11 |
Multiresolution CNN |
0.9828 | 0.124431 | 0.9636 | – | – | – |
Classification of Malware by Using Structural Entropy on Convolutional Neural Networks
|
danielgibert/mlw_classification_structural_entropy |
2018 |
| 12 |
CNN+BiLSTM |
0.9820 | 0.0744 | 0.9605 | – | – | – |
A Hierarchical Convolutional Neural Network for Malware Classification
|
|
2019 |
| 13 |
Scaled bytes sequence + CNN & Bidirectional LSTM |
0.9814 | – | 0.9662 | – | – | – |
HYDRA: A multimodal deep learning framework for malware classification
|
danielgibert/mlw_classification_hydra |
2020 |
| 14 |
Grayscale images + Opcode N-grams (Feature selection for malware classification) |
0.9770 | – | – | – | – | – |
Orthrus: A Bimodal Learning Architecture for Malware Classification
|
danielgibert/mlw_classification_orthrus |
2020 |
| 15 |
DeepConv |
0.9756 | 0.1602 | 0.9071 | – | – | – |
A Hierarchical Convolutional Neural Network for Malware Classification
|
|
2019 |
| 16 |
Gray-scale IMG CNN |
0.9750 | 0.184483 | 0.9400 | 0.973 | – | – |
Using Convolutional Neural Networks for Classification of Malware represented as Images
|
danielgibert/mlw_classification_cnn_img |
2018 |
| 17 |
Hierarchical Attention Network |
0.9742 | 0.0933 | 0.9468 | – | – | – |
A Hierarchical Convolutional Neural Network for Malware Classification
|
|
2019 |
| 18 |
Structural entropy CNN |
0.9708 | 0.134624 | 0.9314 | – | – | – |
Classification of Malware by Using Structural Entropy on Convolutional Neural Networks
|
danielgibert/mlw_classification_structural_entropy |
2018 |
| 19 |
Narayanan et al. (2016): PCA features + 1-NN |
0.9660 | – | 0.9102 | – | – | – |
HYDRA: A multimodal deep learning framework for malware classification
|
danielgibert/mlw_classification_hydra |
2020 |
| 20 |
Deep Transferred Generative Adversarial Networks |
0.9639 | – | – | – | – | – |
Orthrus: A Bimodal Learning Architecture for Malware Classification
|
danielgibert/mlw_classification_orthrus |
2020 |