A genetic-algorithm feature augmentation method is added to ANN, CNN, and RNN malware classifiers, with reported accuracy gains on a 2020 dataset, but the claimed concept drift handling is not supported by temporal train/test evaluation.
Fesad ran- somware detection framework with machine learning using adap- tion to concept drift
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Deep Learning-Driven Malware Classification with API Call Sequence Analysis and Concept Drift Handling
A genetic-algorithm feature augmentation method is added to ANN, CNN, and RNN malware classifiers, with reported accuracy gains on a 2020 dataset, but the claimed concept drift handling is not supported by temporal train/test evaluation.