MalPurifier combines diversified adversarial perturbations, protective noise injection, and a denoising autoencoder with dual loss to defend Android malware detectors, reporting over 90.91% robust accuracy against 37 evasion attacks on two datasets.
Enhancing robustness of deep neural networks against adversarial malware samples: Principles, framework, and application to aics’2019 challenge
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MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks
MalPurifier combines diversified adversarial perturbations, protective noise injection, and a denoising autoencoder with dual loss to defend Android malware detectors, reporting over 90.91% robust accuracy against 37 evasion attacks on two datasets.