Document Type

Article

Publication Date

2024

DOI

10.1109/ACCESS.2024.3494545

Publication Title

IEEE Access

Volume

12

Pages

169432-169441

Abstract

Due to the popularity of Android mobile devices over the past ten years, malicious Android applications have significantly increased. Systems utilizing machine learning techniques have been successfully applied for Android malware detection to counter the constantly changing Android malware threats. However, attackers have developed new strategies to circumvent these systems by using adversarial attacks. An attacker can carefully craft a malicious sample to deceive a classifier. Among the evasion attacks, there is the more potent one, which is based on solid optimization constraints: the Carlini-Wagner attack. Carlini-Wagner is an attack that uses margin loss, which is more efficient than cross-entropy loss. We propose a model based on the Wasserstein Generative Adversarial Network to prevent adversarial attacks in an Android field in a white box scenario. Experimental results show that our method can effectively prevent this type of attack.

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. A. (2024). A defensive strategy against Android adversarial malware attacks. IEEE Access, 12, 169432-169441. https://doi.org/10.1109/ACCESS.2024.3494545

ORCID

0000-0002-8789-0610 (Shetty)

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