A Context-Aware Multimodal Explainable Deep Learning Framework For Robust Android Malware Detection And Proactive Threat Prevention

With the rapid expansion of Android applications, malware attacks targeting mobile devices have increased significantly, creating serious security and privacy concerns for users. Traditional malware detection approaches, such as signature-based and rule-based methods, often fail to detect newly emerging or obfuscated malware variants. To overcome these challenges, this study proposes an explainable artificial intelligence-based framework, named XAI-Droid, for effective Android malware detection and classification. The proposed system integrates deep learning techniques with explainable AI (XAI) methods to enhance detection accuracy while ensuring transparency and interpretability in decision-making. Feature extraction is carried out using static analysis techniques, and the extracted features are used to train advanced machine learning and deep learning models. To improve trust and reliability, explanation methods such as feature importance analysis are incorporated to identify the key attributes influencing classification outcomes. Experimental results demonstrate that the proposed framework achieves high detection accuracy while maintaining interpretability, making it suitable for practical cybersecurity applications. By combining strong classification performance with explainability, XAI-Droid contributes to the development of reliable and trustworthy AI-based mobile security systems.