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Android Malware Detection Using Machine Learning and Reverse Engineering

Authors

Michal Kedziora, Paulina Gawin, Michal Szczepanik and Ireneusz Jozwiak, Wroclaw University of Science and Technology, Poland

Abstract

This paper is focused on the issue of malware detection for Android mobile system by Reverse Engineering of java code. The characteristics of malicious software were identified based on a collected set of applications. Total number of 1958 applications where tested (including 996 malware apps). A unique set of features was chosen. Five classification algorithms (Random Forest, SVM, K-NN, Nave Bayes, Logistic Regression) and three attribute selection algorithms were examined in order to choose those that would provide the most effective malware detection.

Keywords

Malware Detection, Android, Random Forest, SVM, K-NN, Naive Bayes, Logistic Regression

Full Text  Volume 8, Number 17