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Despite the promising results of machine learning models in malwaredetection, they face the problem of concept drift due to malware constantevolution. This leads to a decline in performance over time, as the datadistribution of the new files differs from the training one, requiring regularmodel update. In this work, we propose a model-agnostic protocol to improve abaseline neural network to handle with the drift problem. We show theimportance of feature reduction and training with the most recent validationset possible, and propose a loss function named Drift-Resilient BinaryCross-Entropy, an improvement to the classical Binary Cross-Entropy moreeffective against drift. We train our model on the EMBER dataset (2018) andevaluate it on a dataset of recent malicious files, collected between 2020 and2023. Our improved model shows promising results, detecting 15.2% more malwarethan a baseline model.

Neural Networks Optimizations Against Concept and Data Drift in Malware Detection