This research paper presents MLDroid—a web-based framework—which helps to detect malware from Android devices. Due to increase in the popularity of Android devices, malware developers develop malware on daily basis to threaten the system integrity and user’s privacy. The proposed framework detects malware from Android apps by performing its dynamic analysis. To detect malware from real-world apps, we trained our proposed framework by selecting features which are gained by implementing feature selection approaches. Further, these selected features help to build a model by considering different machine learning algorithms. Experiment was performed on 5,00,000 plus Android apps. Empirical result reveals that model developed by considering all the four distinct machine learning algorithms parallelly (i.e., deep learning algorithm, farthest first clustering, Y-MLP and nonlinear ensemble decision tree forest approach) and rough set analysis as a feature subset selection algorithm achieved the highest detection rate of 98.8% to detect malware from real-world apps.

该研究论文介绍了MLDroid（基于Web的框架），可帮助检测来自Android设备的恶意软件。由于Android设备的日益普及，恶意软件开发人员每天都会开发恶意软件，以威胁系统完整性和用户隐私。拟议的框架通过执行动态分析来检测Android应用中的恶意软件。为了从现实世界的应用程序中检测恶意软件，我们通过选择通过实施功能选择方法获得的功能来训练我们提出的框架。此外，这些选定的功能还可以通过考虑不同的机器学习算法来帮助构建模型。实验在500万个Android应用程序上进行。实证结果表明，该模型是通过并行考虑所有四种不同的机器学习算法（即深度学习算法，