Generative AI creates synthetic malware to boost detection accuracy
The paper proposes using variational autoencoders to generate realistic malware variants, augmenting scarce training data for machine‑learning detectors such as Random Forest, XGBoost, and sequential models. Evaluations show notable improvements in accuracy, precision, recall, and F1 scores, illustrating generative AI’s promise for proactive malware defense.