3DMalDroid: A novel 3D image based approach for android malware detection and classification

Abstract

Android is one of the most widely preferred and utilized operating systems today. Consequently, it has attracted the attention of hackers, and Android device users are increasingly subjected to cyberattacks. This study aims to develop a solution for malware attacks targeting Android-based devices. To achieve this, we propose two novel deep learning-based systems that utilize 2D+ and 3D images for malware detection and malware category classification. The system yielding the best results, which is based on 3D imaging, is named 3DMalDroid. Furthermore, we address imbalanced data and duplicated data issues, which contribute to bias and overfitting in malware detection and classification results. The results demonstrate that the proposed 3DMalDroid system surpasses state-of-the-art studies in the literature, achieving an accuracy of 0.994, precision of 0.993, recall of 0.992, and an F1-score of 0.993. In conclusion, the proposed 3DMalDroid system makes a significant contribution to Android malware detection by addressing duplicate data and class imbalance issues.