Android malware detection through hybrid features fusion and ensemble classifiers: The AndroPyTool framework and the OmniDroid dataset

Volume: 52, Pages: 128 - 142
Published: Dec 1, 2019
Abstract
Cybersecurity has become a major concern for society, mainly motivated by the increasing number of cyber attacks and the wide range of targeted objectives. Due to the popularity of smartphones and tablets, Android devices are considered an entry point in many attack vectors. Malware applications are among the most used tactics and tools to perpetrate a cyber attack, so it is critical to study new ways of detecting them. In these detection...
Paper Details
Title
Android malware detection through hybrid features fusion and ensemble classifiers: The AndroPyTool framework and the OmniDroid dataset
Published Date
Dec 1, 2019
Volume
52
Pages
128 - 142
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