ISSN: 0974 - 9330 (Online); 0975 - 2307 (Print)
http://airccse.org/journal/ijnsa.html
MALWARE DETECTION USING MACHINE LEARNING ALGORITHMS AND REVERSE ENGINEERING OF ANDROID JAVA CODE
Michal Kedziora, Paulina Gawin, Michal Szczepanik and Ireneusz Jozwiak
Faculty of Computer Science and Management Wroclaw, University of Science and Technology Wroclaw, Poland
Abstract:
This research paper is focused on the issue of mobile application malware detection by Reverse Engineering of Android java code and use of Machine Learning algorithms. The malicious software characteristics were identified based on a collected set of total number of 1958 applications (including 996 malware applications). During research a unique set of features was chosen, then three attribute selection algorithms and five classification algorithms (Random Forest, K Nearest Neighbors, SVM, Nave Bayes and Logistic Regression) were examined to choose algorithms that would provide the most effective rate of malware detection.
Original Source Link: https://aircconline.com/ijnsa/V11N1/11119ijnsa01.pdf
Volume Link: http://airccse.org/journal/jnsa19_current.html
http://airccse.org/journal/ijnsa.html
MALWARE DETECTION USING MACHINE LEARNING ALGORITHMS AND REVERSE ENGINEERING OF ANDROID JAVA CODE
Michal Kedziora, Paulina Gawin, Michal Szczepanik and Ireneusz Jozwiak
Faculty of Computer Science and Management Wroclaw, University of Science and Technology Wroclaw, Poland
Abstract:
This research paper is focused on the issue of mobile application malware detection by Reverse Engineering of Android java code and use of Machine Learning algorithms. The malicious software characteristics were identified based on a collected set of total number of 1958 applications (including 996 malware applications). During research a unique set of features was chosen, then three attribute selection algorithms and five classification algorithms (Random Forest, K Nearest Neighbors, SVM, Nave Bayes and Logistic Regression) were examined to choose algorithms that would provide the most effective rate of malware detection.
Original Source Link: https://aircconline.com/ijnsa/V11N1/11119ijnsa01.pdf
Volume Link: http://airccse.org/journal/jnsa19_current.html
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