Title: Enhanced Android security with real-time cloud-driven malware detection and multi-model ensemble predictions

Authors: Isaac Olusegun Osunmakinde

Addresses: Computer Science Department, College of Science, Engineering and Technology, Norfolk State University, Norfolk, Virginia 23504, USA

Abstract: This study addresses the growing sophistication of cyber threats targeting Android devices. This demands more robust security systems. Existing solutions often struggle with high false alarm rates and limited adaptability to novel threats. This research proposes a multi-model ensemble framework that integrates decision trees (DT), random forests (RF), and neural networks (NN) for real-time predictive threat analytics. The system incorporates data preprocessing techniques (log transformation and SMOTE) to address data imbalances and utilises iterative feature selection to optimise performance. Experimental validation on Android traffic and zero-day ransomware datasets demonstrates the model's effectiveness. The ensemble model achieves 98.59% accuracy on Android malware detection with a 1.49% false alarm rate, outperforming individual models and previous studies. The framework achieves 99.41% test accuracy for zero-day threat detection, with a false alarm rate of only 0.51%, confirming its high generalisability. A cloud-based setup enables ongoing, scalable, and proactive cybersecurity for Android and other platforms.

Keywords: Android security; ensemble model; malware detection; real-time; cloud-driven; cybersecurity; machine learning.

DOI: 10.1504/IJAISC.2025.149627

International Journal of Artificial Intelligence and Soft Computing, 2025 Vol.9 No.2, pp.178 - 207

Received: 09 Nov 2024
Accepted: 27 Jun 2025
Published online: 07 Nov 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article