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Enhancing robustness of deep learning-based malware detection against adversarial attacks

Dennis Benedict Crasta Vikash Kumar
Aug 2026 · Journal of Computer Virology and Hacking Techniques · Vol 22 · 0 citations · 31 references

TL;DR

This paper introduces DefendMal, a novel framework that synergistically combines Denoise Autoencoder with Sequence Squeezing, a Context-aware Adversarial Generator (CAG-AdvGAN), Projected Gradient Descent (PGD) adversarial training, and a Positive–Negative Detector with Variational Autoencoder (PNDetector-VAE) to enhance robustness against evolving adversarial threats.

DOI Semantic Scholar
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