DroidMD: An efficient and scalable Android malware detection approach at source code level

Security researchers and anti-virus industries have speckled stress on an Android malware, which can actually damage your phones and threatens the Android markets. In this paper, we propose and develop DroidMD, a scalable self-improvement based tool, based on auto optimisation of signature set, which detect malicious apps in the market at source code level. A prototype has been developed tested and implemented to detect malware in applications. We implement and evaluate our approach on almost 30,000 applications including 27,000 benign and 3,670 malware applications. DroidMD detects malware in different applications at partial level and full level. It analyses only the applications code, which increase its reliability. Our evaluation of DroidMD demonstrates that our approach is very efficient in detecting malware at large scale with high accuracy of 95.5%. © 2021 Inderscience Enterprises Ltd.