Abstract
Mobile malware is considered as one of the crucial security challenges due to its high volume and quick variety, especially on the Android platform. Many researches have been proposed to detect malware, but some of them suffer low detection accuracy or high time consumption. This research implements an effective mobile malware detection framework by proposing a new feature selection method, which is term frequency-sample frequency differentiation (TF-SFD), to reduce the features with little importance. In addition, a false positive rate (FPR) filter is proposed based on sample frequency differentiation (SFD) for reducing FPR. We investigate four machine learning methods and the experimental results show that the TF-SFD combining with random forest (RF) classifier performs best in terms of accuracy in detecting malware on Android, which obtains 92.54% testing accuracy.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Mobile Network Design and Innovation
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
