Android application evolution and malware detection
Android has dominated the mobile market for a few years now, and continues to increase its market share. Meanwhile, Android has seen a sharper increase in malware. It is a matter of utmost urgency to find a better way to detect Android malware. In this thesis, we use static code analysis to extract the android application security features and two different classification models to detect Android malware. Our permissions-based classification model can achieve 96.5% accuracy, 97.2% TPR and 95.5% TNR with low