Defensive Randomization Against Adversarial Attacks in Image-Based Android Malware Detection

• Published in: ICC 2023 - IEEE International Conference on Communications pp. 5072 - 5077
• Main Authors: Lan, Tianwei, Darwaish, Asim, Nait-Abdesselam, Farid, Gu, Pengwenlong
• Format: Conference Proceeding
• Language: English
• Published: IEEE 28.05.2023
• Subjects: Adversarial Attacks and Defense; Android Malware Detection; Biological system modeling; Closed box; Computer security; Deep Learning; Ecosystems; Machine learning; Malware; Operating systems; Randomization
• ISSN: 1938-1883
• DOI: 10.1109/ICC45041.2023.10279592

The extensive popularity of Android operating system hones the increased malware attacks and threatens the Android ecosystem. Machine learning is one of the versatile tools to detect legacy and new malware with high accuracy. However, these Machine Learning (ML) models are vulnerable to adversarial attacks, which severely threaten their cybersecurity deployment. To combat the deterrence of ML models against adversarial attacks, we propose a novel randomization method as a defense for image-based detection systems. In addition to defensive randomization, the paper also introduces a novel method, called AutoE, for transforming an APK to an image by leveraging API calls only. To evaluate the effectiveness of randomization as a defense against adversarial settings, we compare our AutoE with two state-of-the-art image-based Android malware detection systems. The experimental results reveal that the randomization is a strong defensive hood for image-based Android malware detection systems against adversarial attacks. Moreover, our novel AutoE detects malware with 96% accuracy and the randomization approach makes it harder against adversarial attacks.