JOIV : International Journal on Informatics Visualization
Vol 6, No 1 (2022)

An Android Malware Detection System using a Knowledge-based Permission Counting Method

Sun-A Lee (Department of Smart Information and Telecommunication Engineering, Sangmyung University, Cheonan, Chungnam, Republic of Korea)
A-Reum Yoon (Department of Smart Information and Telecommunication Engineering, Sangmyung University, Cheonan, Chungnam, Republic of Korea)
Ji-Won Lee (Department of Smart Information and Telecommunication Engineering, Sangmyung University, Cheonan, Chungnam, Republic of Korea)
Kwangjae Lee (Department of Smart Information and Telecommunication Engineering, Sangmyung University, Cheonan, Chungnam, Republic of Korea)



Article Info

Publish Date
26 Mar 2022

Abstract

As the number of cases of damage caused by malicious apps increases, accurate detection is required through various detection conditions, not just detection using simple techniques. In this paper, we propose a knowledge-based machine learning method using authority information and adding its usage counting features. This method is classifying training apps and malicious apps through machine learning using permission features in manifest.xml of Android apps. As a result of the experiment, accuracy, recall, precision, F1 score are 99.01%, 97.70%, 100.0%, 99.01%, respectively. Since Recall is higher than other indicators, it accurately predicts malicious apps as malicious. In other words, the proposed system is effective in preventing the distribution of malicious apps.

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Journal Info

Abbrev

joiv

Publisher

Subject

Computer Science & IT

Description

JOIV : International Journal on Informatics Visualization is an international peer-reviewed journal dedicated to interchange for the results of high quality research in all aspect of Computer Science, Computer Engineering, Information Technology and Visualization. The journal publishes state-of-art ...