The study proposes an efficient and secure hybrid framework for detecting Android malware in modern mobile environments. The widespread adoption of Android smartphones has led to increased security risks, as these devices are frequently targeted by sophisticated malware attacks. Furthermore, the growing integration of Android applications with Internet of Things (IoT) systems amplifies the potential impact of such threats. Detecting malware manually in large-scale and continuously evolving datasets is both time-consuming and ineffective. To address these challenges, our approach integrates real-time data acquisition and deep learning techniques. Malware hash values are dynamically updated using data extracted from Twitter at regular intervals of 48 hours, ensuring the system remains up-to-date with emerging threats. In addition, application features, particularly permissions, are analyzed using a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) architecture for accurate classification. The model is trained and evaluated to distinguish between benign and malicious applications, achieving a detection accuracy of approximately 94%. The proposed multi-layer framework enhances detection efficiency by combining traditional signature-based methods with intelligent learning mechanisms. This integrated system improves reliability, strengthens mobile security, and provides an effective solution for real-time Android malware detection and prevention.
Rao et al. (2026) studied this question.