Advancing Android Malware Detection: A Unified Framework with Dynamic Feature Extraction and Privacy-Preserving Collaboration

Malware classification presents a pressing cybersecurity challenge, necessitating advanced pattern recognition techniques. This paper introduces a novel framework that integrates supervised machine learning with deep learning methodologies, augmented by advanced image processing techniques. Beyond traditional methods, our framework pioneers dynamic feature extraction with adversarial resilience, continuously adapting to evolving malware behaviors in real-time. Additionally, we propose a graph-based behavioral analysis for malware graphs, capturing intricate relationships and dependencies between system entities to enhance detection accuracy. By modeling malware behavior as a graph structure and employing graph neural networks, our framework offers a comprehensive approach to malware analysis, uncovering sophisticated attack patterns with unparalleled efficacy. Furthermore, our framework embraces privacy-preserving collaborative malware analysis, enabling secure knowledge sharing across organizations without compromising sensitive data. Through federated learning and secure multiparty computation, participating entities can collaboratively train detection models while preserving the confidentiality of their proprietary datasets. Through extensive experimentation on diverse malware datasets, our framework demonstrates superior classification accuracy and performance metrics, outperforming existing methods significantly. We highlight its utility for real-world cyber protection, emphasizing scalability and adaptability to changing malware environments. By presenting a unified and innovative approach to malware classification through comprehensive pattern recognition and incorporating dynamic feature extraction, graph-based analysis, and privacy-preserving collaboration, this paper advances the field and provides valuable insights for securing our digital world.