Abstract: On Android, every application operates during a basic sandbox and is prevented from accessing additional services that need users' consent. These services can only be accessed if users allow the appliance to use them. Granting of permission is static and may only be done at the time of the installation of the application. Android security model leaves most of the labor for security-related approaches to the user. LibreAV is an endeavor to detect malware on Android devices employing a machine learning approach that is powered by the Tensor flow. We use a two-layer neural network trained with a set of features. The neural network is tuned in such the simplest way that it performs efficiently on mobile devices where computational resources are limited. Testes show that LibreAV performs efficiently and effectively even on low-end mobile devices. With LibreAV, you’ll be able to scan all the installed apps on your device in an exceedingly matter of seconds. It also encompasses a real-time scan feature that alerts you whenever an app is installed or updated. Neural networks and SHA scan the applications and predict potential malicious behavior using state-of-the-art Machine Learning algorithms.
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DOI:
10.17148/IJARCCE.2022.11655
