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

模型名称	Accuracy (10-fold)	Paper Title	Repository
Grayscale images + Opcode N-grams (Feature selection for malware classification)	0.9770	Orthrus: A Bimodal Learning Architecture for Malware Classification
Ahmadi et al. (2016): API feature vector + XGBoost	0.9868	HYDRA: A multimodal deep learning framework for malware classification
Structural entropy CNN	0.9708	Classification of Malware by Using Structural Entropy on Convolutional Neural Networks
CNN+BiLSTM	0.9820	A Hierarchical Convolutional Neural Network for Malware Classification	-
LBP features + XGBoost	-	Using Convolutional Neural Networks for Classification of Malware represented as Images
CNN BiLSTM - Reb Sampl	-	Deep learning at the shallow end: Malware classification for non-domain experts
Hierarchical Attention Network	0.9742	A Hierarchical Convolutional Neural Network for Malware Classification	-
Scaled bytes sequence + CNN & Bidirectional LSTM	0.9814	HYDRA: A multimodal deep learning framework for malware classification
Opcode-based Shallow CNN	0.9917	Convolutional Neural Network for Classification of Malware Assembly Code
Haralick features + XGBoost	-	Using Convolutional Neural Networks for Classification of Malware represented as Images
MalConv	0,9641	A Hierarchical Convolutional Neural Network for Malware Classification	-
Deep Transferred Generative Adversarial Networks	0.9639	Orthrus: A Bimodal Learning Architecture for Malware Classification
SEA	0.9912	Sequential Embedding-based Attentive (SEA) classifier for malware classification
Zero Rule Classifier	0.2707	HYDRA: A multimodal deep learning framework for malware classification
Orthrus	0.9924	Orthrus: A Bimodal Learning Architecture for Malware Classification
Multiresolution CNN	0.9828	Classification of Malware by Using Structural Entropy on Convolutional Neural Networks
Gray-scale IMG CNN	0.9750	Using Convolutional Neural Networks for Classification of Malware represented as Images
Random Guess Classifier	0.1755	HYDRA: A multimodal deep learning framework for malware classification
Narayanan et al. (2016): PCA features + 1-NN	0.9660	HYDRA: A multimodal deep learning framework for malware classification
Autoencoders+Residual Network	0.9861	An End-to-End Deep Learning Architecture for Classification of Malware’s Binary Content	-

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