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All Journal JOIV : International Journal on Informatics Visualization
Sun-A Lee
Department of Smart Information and Telecommunication Engineering, Sangmyung University, Cheonan, Chungnam, Republic of Korea
Computer Science & IT

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An Android Malware Detection System using a Knowledge-based Permission Counting Method Sun-A Lee; A-Reum Yoon; Ji-Won Lee; Kwangjae Lee
JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1.859

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.
Co-Authors A-Reum Yoon Ji-Won Lee Kwangjae Lee