ANDROID MALWARE DETECTION USING ENSEMBLE LEARNING
Abstract
Android has endured to benefit popularity among cell phone users international.At the same time there has been a rise in malware focused on the platform, with greater current lines employing surprisingly state-ofthe-art detection avoidance strategies.As traditional signature primarily based strategies emerge as much less strong in detecting unknown malware, options are wanted for timely 0-day discovery.Accordingly, this paper proposes a method that utilizes ensemble learning for Android malware detection.It combines benefits of static analysis with the efficiency and performance of ensemble device studying to enhance Android malware detection accuracy.The machine getting to know models are built using a huge repository of malware samples and benign apps.
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