Shirina Samreen

Android Malware Detection Using CTGAN-Based Data Augmentation and Autoencoder-Driven Feature Extraction

Experimental results demonstrate that the combined CTGAN and autoencoder pipeline significantly improves minority-class detection while maintaining high overall accuracy, and highlight that integrating generative augmentation with learned feature representations is an effective strategy for handling high-dimensional, imbalanced Android malware datasets.