Tahtaci, B.Canbay, B.2023-10-192023-10-192020159781728191362https://doi.org/10.1109/ASYU50717.2020.9259834https://hdl.handle.net/20.500.12469/49382020 Innovations in Intelligent Systems and Applications Conference, ASYU 2020 --15 October 2020 through 17 October 2020 -- --165305The usage of mobile devices is increasing exponentially. There were lots of critical applications such as banking to health applications are available on mobile devices through mobile applications. This penetration and spread of mobile applications brings some threats. Malicious software(Malware) is one of these dangers. Malware has the potential to cause damage to various scales such as theft of sensitive data, identity and credit card. To reduce the effects of these threats, antiviruses have been developed and malware analysis teams have been established, but human effort may be insufficient in the rapidly growing malware market. For this reason, automated malware scanning solutions should be developed by making use of machine learning algorithms. In this study, machine learning models were created by using the n-gram features of the smali files, which are the decompiled Android packages. The trained models are combined with different feature extraction and feature selection methods and as a result their performances are reported. © 2020 IEEE.trinfo:eu-repo/semantics/closedAccessArtificial Neural NeworksMachine LearningMalware DetectionMobile MalwaresRandom ForestStatic Code AnalysisAndroid (operating system)Data privacyFeature extractionIntelligent systemsLearning algorithmsMachine learningMalwareMobile computingAndroid malwareCredit cardsCritical applicationsFeature selection methodsMachine learning modelsMalware analysisMobile applicationsSensitive datasMobile securityConference Object10.1109/ASYU50717.2020.92598342-s2.0-85097952821N/AN/A