Design of secure and robust cognitive system for malware detection. (arXiv:2208.02310v1 [cs.CR])

Machine learning based malware detection techniques rely on grayscale images
of malware and tends to classify malware based on the distribution of textures
in graycale images. Albeit the advancement and promising results shown by
machine learning techniques, attackers can exploit the vulnerabilities by
generating adversarial samples. Adversarial samples are generated by
intelligently crafting and adding perturbations to the input samples. There
exists majority of the software based adversarial attacks and defenses. To
defend against the adversaries, the existing malware detection …

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