MALICIOUS ANDROID APPS DETECTION USING MACHINE LEARNING
Автор: Bilal Ahmad Kamal, Adeela Muhammad Askari, Salman Tariq
Организация: Air University Islamabad Pakistan
Аннотация. The use of smartphones has wisely evolved in the 20th century. Many people all
over the world can connect to their smartphones in a variety of ways. Some invaders are
leveraging the power of the rapidly growing smartphone usage to Developing rogue Android
applications to steal handsets' sensitive data To address these grave issues, a malicious program
that is both effective and efficient is required. Numerous malware detection programs have
historically been built, but some of them are not able to identify recently developed malware
programs or programs contaminated with different Trojan horses, worms, and spyware. The
software for detecting fraudulent programs can be enhanced especially thanks to ML
algorithms.
The system uses ML classification algorithms like Support Vector Machine (SVM) to improve
the malware application detection in the proposed system. The proposed ML classification and
fusion algorithms will improve performance metrics like the accuracy of malware application
exposure and decrease the complexity of the detection process. The suggested approach
integrates detecting software with a training application that users can install on Android
cellphones to flag hazardous activities when they are accessible.
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