Malware Detection for Android Systems using Deep Learning
Renugadevi Rajaram, Sk Sajida Sultana, Anusha Kakumanu, S.Pavan Manohar, P.Sudha Rani, G. Yaswanth · 2024
Android’s irresistible acceptance as the most popular smartphone operating system has brought about a surge in malware instances compared to previous years. This surge necessitates robust anti-malware solutions to safeguard users’ sensitive mobile data from such threats. Through examination, various Android malware types and their tactics, coupled with deep learning approaches, have been analyzed for their roles in attacking devices, while antivirus programs have been assessed for their efficacy in protective Android systems. The discussion revolves around various deep acquisition-based techniques for determining Android adwa re, such as Maldozer, Droid-Detector, Droid-Deep-Learner, Deep-Flow, Droid-Delver, and Droid-Deep. The goal is to create a deep learning model that can detect whether an Android application has malware automatically and doesn’t need to be installed.