PROJECT TITLE :

High accuracy Android Malware Detection using Ensemble Learning

ABSTRACT:

With over 50 billion downloads and more than one.three million apps in Google's official market, Android has continued to achieve popularity among smartphone users worldwide. At the identical time there has been an increase in malware targeting the platform, with a lot of recent strains employing highly refined detection avoidance techniques. As ancient signature-based mostly ways decrease potent in detecting unknown malware, alternatives are needed for timely zero-day discovery. So, this study proposes an approach that utilises ensemble learning for Android malware detection. It combines blessings of static analysis with the potency and performance of ensemble Machine Learning to improve Android malware detection accuracy. The machine learning models are built employing a large repository of malware samples and benign apps from a leading antivirus vendor. Experimental results and analysis presented shows that the proposed technique which uses a massive feature house to leverage the power of ensemble learning is capable of ninety seven.3-ninety ninep.c detection accuracy with terribly low false positive rates.