Hybrid Deep Learning Framework for Android Malware Detection Using Application Permissions and Social Media Threat Intelligence

The rapid growth of smartphone usage has made mobile devices an essential part of everyday life, supporting activities such as communication, online banking, education, and social networking. However, the increasing popularity of Android-based devices has also made them a major target for cyber attackers who develop malicious applications to exploit system vulnerabilities and steal sensitive information. To address this challenge, an intelligent malware detection and prevention framework for Android devices is proposed. The proposed system integrates real-time threat intelligence gathered from social media platforms with deep learning-based malware classification techniques. Malware signatures shared through social media sources are periodically collected and stored in a centralized malware hash database to ensure the system remains updated with newly discovered threats. In addition, the system employs a deep learning model based on a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) architecture to analyze Android application permissions and classify applications as benign or malicious. By combining real-time malware signature updates with deep learning-based behavioural analysis, the proposed framework enhances the accuracy and efficiency of Android malware detection. Experimental evaluation demonstrates that the system achieves high detection accuracy and provides a robust solution for protecting Android devices against emerging malware threats.