Please use this identifier to cite or link to this item: https://repository.rsif-paset.org/xmlui/handle/123456789/360
Title: Mid@ndro: a Middleware Architecture for Malware Detection on Android
Authors: Sawadogo, Zakaria
Thioye, Babacar
Gaye, Ibrahima
Dembele, Jean-Marie
Mendy, Gervais
Ouya, Samuel
Keywords: Middle-ware , Android malware detection , Machine learning , Cyber-security , Antivirus
Issue Date: 29-Dec-2023
Publisher: IEEE Xplore
Abstract: Android is a highly popular platform for mobile devices; however, it is also vulnerable to malware attacks due to the platform's ability to allow users to install apps from unverified sources. This increases the risk of downloading and installing malicious apps on devices. To tackle this issue, security researchers and software developers have proposed several solutions, such as antivirus and anti-malware software, app scanning services, and more secure app distribution channels. Our proposal for addressing the challenge of detecting Android malware is through a middle-ware architecture that empowers users to make informed decisions about which apps to install and run on their device. This middle-ware would thoroughly analyze apps for potential security risks, provide users with a comprehensive understanding of the app's behavior, permissions, and potential risks, and based on this analysis, enable users to make informed decisions on whether or not to allow the app to continue running on their device.
Description: Conference proceeding full text: https://doi.org/10.1109/CloudTech58737.2023.10366106
URI: https://repository.rsif-paset.org/xmlui/handle/123456789/360
Appears in Collections:ICTs including Big Data and Artificial Intelligence

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