Decision tree based android malware detection system

Developments in mobile device technology are driving mobile malware development especially on popular operating system platforms such as Android. Defensive software developed for malware is limited due to insufficient understanding of the features of malicious software and inaccessible on time to relevant examples. In this study, Android malware and detection methods were investigated. In this work, a decision tree based Android malware detection system was developed using C4.5 and Hoeffding tree algorithms. In the developed system, the success rate of the C4.5 decision tree algorithm was 95.862% and the success rate of the Hoeffding tree algorithm was 93.187%.