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SotA/Miscellaneous/Malware Classification/Microsoft Malware Classification Challenge

Malware Classification on Microsoft Malware Classification Challenge

Metric: Macro F1 (10-fold) (higher is better)

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#Model↕Macro F1 (10-fold)▼Extra DataPaperDate↕Code
1HYDRA0.9951No--Code
2Zhang 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)0.9938No--Code
3Ahmadi 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)0.9931No--Code
4SEA0.9908NoSequential Embedding-based Attentive (SEA) class...2023-02-11Code
5Orthrus0.9872No--Code
6Opcode-based Shallow CNN0.9856No--Code
7Hierarchical Convolutional Network0.983No---
8Dynamic Time Wrapping + K-NN0.9813No--Code
9Autoencoders+Residual Network0.9719No---
10Scaled bytes sequence + CNN & Bidirectional LSTM0.9662No--Code
11Ahmadi et al. (2016): API feature vector + XGBoost0.9638No--Code
12Multiresolution CNN0.9636No--Code
13CNN+BiLSTM0.9605No---
14Hierarchical Attention Network0.9468No---
15Gray-scale IMG CNN0.94No--Code
16Structural entropy CNN0.9314No--Code
17Narayanan et al. (2016): PCA features + 1-NN0.9102No--Code
18DeepConv0.9071No---
19MalConv0.8902No---