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http://dx.doi.org/10.22156/CS4SMB.2022.12.03.017

A Study on Android Malware Detection using Selected Features  

Myeong, Sangjoon (Graduate School of Information and Communication Technology, Ajou University)
Kim, Kangseok (Department of Cyber Security, Ajou University)
Publication Information
Journal of Convergence for Information Technology / v.12, no.3, 2022 , pp. 17-24 More about this Journal
Abstract
Mobile malicious apps are increasing rapidly, and Android, which accounts for most of the global mobile OS market, is becoming a major target of mobile cyber security threats. Therefore, in order to cope with rapidly evolving malicious apps, there is a need for detection techniques of malicious apps using machine learning, one of artificial intelligence implementation technologies. In this paper, we propose a selected feature method using feature selection and feature extraction that can improve the detection performance of malicious apps. In the feature selection process, the detection performance improved according to the number of features, and the API showed relatively better detection performance than the permission. Also combining the two characteristics showed high precision of over 93% on average, confirming that the appropriate combination of characteristics could improve the detection performance.
Keywords
Android Malware; Machine Learning; Feature Selection; Feature Extraction; Information Security;
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Times Cited By KSCI : 4  (Citation Analysis)
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