Résumé:
The widespread use of Android devices has made them a prime target for malware,
highlighting the critical need for effective detection mechanisms to protect users and
their data. This thesis introduces an innovative mixed static analysis approach that
leverages machine learning technique, specifically, ensemble learning, for Android malware
detection, which integrates permission analysis, opcode examination, and bytecode
visualization, capitalizing on the strengths of each method. Our comprehensive approach
aims to enhance detection accuracy and adaptability, effectively countering the evolving
tactics of malware developers, and providing an effective Zero-Day threat defense
mechanism. Extensive experiments conducted on two datasets containing malware
samples from different time periods demonstrate the superior performance of our method.
We achieved a remarkable accuracy of 99.82% on the dataset comprising older malware
samples, showcasing our model’s robustness in handling historical threats. For the
dataset containing recent malware samples, our approach achieved a high accuracy of
96.06%, significantly outperforming other methods which exhibited notable decreases
in performance with newer malware. These findings underscore the effectiveness of our
integrated model in providing a robust defense against a wide range of malware behaviors.
This research contributes significantly to cybersecurity by proposing an advanced and
flexible solution for Android malware detection, offering practical implications for
enhancing mobile device security in real-world applications.
