Malware Classification On Malimg Dataset
Metrics
Accuracy (10-fold)
Macro F1 (10-fold)
Results
Performance results of various models on this benchmark
Model Name | Accuracy (10-fold) | Macro F1 (10-fold) | Paper Title | Repository |
|---|---|---|---|---|
| Gray-scale IMG CNN | 0.9848 | 0.9580 | Using Convolutional Neural Networks for Classification of Malware represented as Images | |
| GA Designed Deep CNN | - | - | Designing Deep Convolutional Neural Networks using a Genetic Algorithm for Image-based Malware Classification | - |
| GRU + SVM | - | - | Towards Building an Intelligent Anti-Malware System: A Deep Learning Approach using Support Vector Machine (SVM) for Malware Classification | - |
| CNN + SVM | - | - | Towards Building an Intelligent Anti-Malware System: A Deep Learning Approach using Support Vector Machine (SVM) for Malware Classification | - |
| FFNN + SVM | - | - | Towards Building an Intelligent Anti-Malware System: A Deep Learning Approach using Support Vector Machine (SVM) for Malware Classification | - |
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