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Malware Classification On Microsoft Malware

评估指标

Accuracy (10-fold)

评测结果

各个模型在此基准测试上的表现结果

Paper TitleRepository
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)0.9976HYDRA: A multimodal deep learning framework for malware classification-
HYDRA0.9975HYDRA: A multimodal deep learning framework for malware classification-
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)0.9974HYDRA: A multimodal deep learning framework for malware classification-
Orthrus0.9924Orthrus: A Bimodal Learning Architecture for Malware Classification-
Opcode-based Shallow CNN0.9917Convolutional Neural Network for Classification of Malware Assembly Code-
Hierarchical Convolutional Network0.9913A Hierarchical Convolutional Neural Network for Malware Classification-
SEA0.9912Sequential Embedding-based Attentive (SEA) classifier for malware classification
Dynamic Time Wrapping + K-NN0.9894Classification of Malware by Using Structural Entropy on Convolutional Neural Networks-
Ahmadi et al. (2016): API feature vector + XGBoost0.9868HYDRA: A multimodal deep learning framework for malware classification-
Autoencoders+Residual Network0.9861An End-to-End Deep Learning Architecture for Classification of Malware’s Binary Content-
Multiresolution CNN0.9828Classification of Malware by Using Structural Entropy on Convolutional Neural Networks-
CNN+BiLSTM0.9820A Hierarchical Convolutional Neural Network for Malware Classification-
Scaled bytes sequence + CNN & Bidirectional LSTM0.9814HYDRA: A multimodal deep learning framework for malware classification-
Grayscale images + Opcode N-grams (Feature selection for malware classification)0.9770Orthrus: A Bimodal Learning Architecture for Malware Classification-
DeepConv0.9756A Hierarchical Convolutional Neural Network for Malware Classification-
Gray-scale IMG CNN0.9750Using Convolutional Neural Networks for Classification of Malware represented as Images-
Hierarchical Attention Network0.9742A Hierarchical Convolutional Neural Network for Malware Classification-
Structural entropy CNN0.9708Classification of Malware by Using Structural Entropy on Convolutional Neural Networks-
Narayanan et al. (2016): PCA features + 1-NN0.9660HYDRA: A multimodal deep learning framework for malware classification-
Deep Transferred Generative Adversarial Networks0.9639Orthrus: A Bimodal Learning Architecture for Malware Classification-
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