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SotA/Miscellaneous/Malware Classification/Microsoft Malware Classification Challenge

Malware Classification on Microsoft Malware Classification Challenge

Metric: Accuracy (10-fold) (higher is better)

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Results

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#Model↕Accuracy (10-fold)▼Extra DataPaperDate↕Code
1MalConv9641No---
2Ahmadi 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)0.9976No--Code
3HYDRA0.9975No--Code
4Zhang 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)0.9974No--Code
5Orthrus0.9924No--Code
6Opcode-based Shallow CNN0.9917No--Code
7Hierarchical Convolutional Network0.9913No---
8SEA0.9912NoSequential Embedding-based Attentive (SEA) class...2023-02-11Code
9Dynamic Time Wrapping + K-NN0.9894No--Code
10Ahmadi et al. (2016): API feature vector + XGBoost0.9868No--Code
11Autoencoders+Residual Network0.9861No---
12Multiresolution CNN0.9828No--Code
13CNN+BiLSTM0.982No---
14Scaled bytes sequence + CNN & Bidirectional LSTM0.9814No--Code
15Grayscale images + Opcode N-grams (Feature selection for malware classification)0.977No--Code
16DeepConv0.9756No---
17Gray-scale IMG CNN0.975No--Code
18Hierarchical Attention Network0.9742No---
19Structural entropy CNN0.9708No--Code
20Narayanan et al. (2016): PCA features + 1-NN0.966No--Code
21Deep Transferred Generative Adversarial Networks0.9639No--Code
22Zero Rule Classifier0.2707No--Code
23Random Guess Classifier0.1755No--Code