HN-DROID: Android Malware Detection Using Novel Feature selection And Ensemble Techniques

As cell phones have become important tools in our today's life, security challenges have become more serious. Malware detection is a set of techniques used to examine and understand how android applications work and to identify malwares. Malware detection using traditional methods is not reliable. So, in this research, the first step is seeking and creating a suitable and large dataset. Then using feature selection and ensemble method tries to provide better malware detection results compared to the other researches. Our opinion for feature selection is to use Random Forest And Mutal information that can reduce the number of features from ۳۵۰۴to ۱۵۰۴. The results show that the combination of classifiers into an ensemble model provides better accuracy than an individual classifier. The malware detection rate is up to ۹۹.۷۵% in our experimental evaluation.