Abstract:
The extensive popularity of Android operating system hones the increased malware attacks and threatens the Android ecosystem. Machine learning is one of the versatile too...Show MoreMetadata
Abstract:
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.
Date of Conference: 28 May 2023 - 01 June 2023
Date Added to IEEE Xplore: 23 October 2023
ISBN Information:
Electronic ISSN: 1938-1883