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romiisromie/latent-malware-defense: Robust adversarial training framework for deep learning malware classifiers. Implements continuous embedding-layer perturbations (FGSM/PGD) on a decoupled MalConv architecture to mitigate feature-space evasion attacks. Evaluates model resilience, latent distribution shifts, and cybersecurity robustness. Designed for ML Security research.

by romiisromie

Robust adversarial training framework for deep learning malware classifiers. Implements continuous embedding-layer perturbations (FGSM/PGD) on a decoupled MalConv architecture to mitigate feature-space evasion attacks. Evaluates model resilience, latent distribution shifts, and cybersecurity robustness. Designed for ML Security research.

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