Malware Detection Using a Random Forest Method Trained on a Balanced Synthetic Dataset

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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Bibliographic Details
Main Authors: Neo Onica Matsobane, Sello Mokwena
Format: Article
Language:English
Published: IMS Vogosca 2025-03-01
Series:Science, Engineering and Technology
Subjects:
malware detection
accuracy
random forest
flow-based model
balanced dataset
synthetic dataset
Online Access:https://setjournal.com/SET/article/view/167
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