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Android Malware Detection with ML by simon.queyrut is a document available to read on EtoBox.

What is Android Malware Detection with ML about?

This study focuses on detecting Android malware using supervised machine learning algorithms, specifically through static analysis of a DREBIN malware dataset. The research evaluates various algorithms including Support Vector Machine (SVM), Naive Bayes, Decision Tree, and K-Nearest Neighbors, with SVM achieving the highest accuracy of 96.94% and an AUC of 95%. The findings suggest that static analysis combined with machine learning is an effective approach for malware detection on the Android platform.

Author

simon.queyrut

Language

EN