Advancing Android Malware Detection with Deep Learning & LLMs in Big Data Ecosystem: A Forward Vision

As the Android operating system dominates the mobile ecosystem, its open-source nature has made it increasingly vulnerable to sophisticated malware attacks. Traditional security techniques, such as static and dynamic code analysis, often fail to detect modern malware due to issues like code obfuscation and limited scalability. This paper explores the limitations of these methods and suggests incorporating advanced Deep Neural Networks as more effective solutions for malware detection in Android environments. Additionally, the integration of Large Language Models (LLMs) offers new possibilities for understanding complex malware behaviors and patterns. We also discuss Android’s security architecture, highlighting key vulnerabilities and attack surfaces. Through this study, we suggest a forward-thinking approach to enhancing Android malware detection using machine learning models, envisioning the future of security in the Android ecosystem as big data and artificial intelligence technologies continue to evolve.