Malware detection in android applications using deep learning neural networks
Gautam Kumar, Srinivas Rithik Ghantasala, Ratnam Dodda, M. Sunitha, A. Basha, Sunil Kumar · 2025
This paper explores Android malware detection using artificial neural networks (ANN), focusing on extracting patterns and system call traces from applications. It employs deep learning architectures to learn features and classify malware effectively. Experimental results across datasets like Drebin and Droidcat show accuracy rates consistently above 90%. Evaluation metrics include accuracy, F1-score, Mathews coefficient constant (MCC), precision, and recall, demonstrating the system’s efficacy that detects both identified and undetected Android malware variants.