Malware Detection Using a Random Forest Method Trained on a Balanced Synthetic Dataset
The accuracy of malware detection is closely related to the available datasets, which are often small and imbalanced. To overcome these challenges, this study proposed a new method that creates synthetic malware data and increases the size and balance by generating several data sets with a flow-base...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IMS Vogosca
2025-03-01
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| Series: | Science, Engineering and Technology |
| Subjects: | |
| Online Access: | https://setjournal.com/SET/article/view/167 |
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