Document Type

Article

Publication Date

2024

DOI

10.1109/ACCESS.2024.3486734

Publication Title

IEEE Access

Volume

12

Pages

162685-162696

Abstract

Adversarial attacks pose significant threats to Android malware detection by undermining the effectiveness of machine learning-based systems. The rapid increase in Android apps complicates the management of malicious software that can compromise user defense solutions. Many current Android defense techniques rely on deep learning methods. Malicious users exploit GAN-based attacks to achieve adversarial attack transferability and deceive target models by crafting adversarial examples based on known models. We propose a new model based on a Cycle Generative Adversarial Network (CycleGAN) to detect GAN-based attacks. This model incorporates a gradient penalty to enhance the detection rate of the target model. Our investigation focuses on a gray box scenario, where the attacker has partial information about the model. The results show that our model outperforms existing classifiers in detecting adversarial attacks.

Comments

The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Office or the U.S. government. The U.S. government is authorized to reproduce and distribute reprints for government purposes notwithstanding any copyright notation herein.

Original Publication Citation

Atedjio, F. S., Lienou, J. P., Nelson, F. F., Shetty, S. S., & Kamhoua, C. (2024). CycleGAN-gradient penalty for enhancing Android adversarial malware detection in gray box setting. IEEE Access, 12, 162685-162696. https://doi.org/10.1109/ACCESS.2024.3486734

ORCID

0000-0002-8789-0610 (Shetty)

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