Malware detection from the smartphone has become a challenging issue for academicians and researchers. In this research paper, we applied five distinct machine learning algorithms and three different ensemble methods to develop a model for detecting malware from an Android-based smartphone. In this study, we proposed a framework that helps in selecting the right sets of the feature with an aim to improve the performance of the malware detection models. The proposed malware detection framework is then validated by considering two distinct performance parameters, i.e., accuracy and F-measure as a benchmark to detect malware from real-world apps. We performed an empirical study on thirty different categories of Android apps. The experimental data set consists of 1,94,659 benign apps and 67,538 malware apps that are collected from different promised repositories. Empirical results reveal that the models developed by using the proposed feature selection framework are able to detect more malware-infected apps when compared to all extracted feature sets. Moreover, the malware detection model build by using nonlinear ensemble decision tree forest (NDTF) approach is achieved a detection rate of 98.8%. In addition to that, the proposed malware detection framework is more effective in detecting malware-infected apps as compared to different anti-virus scanners and different frameworks or approaches developed in the literature.

对于学者和研究人员而言，从智能手机检测恶意软件已成为一个具有挑战性的问题。在这篇研究论文中，我们应用了五种不同的机器学习算法和三种不同的集成方法来开发一种用于从基于Android的智能手机中检测恶意软件的模型。在这项研究中，我们提出了一个框架，该框架有助于选择正确的功能集，以提高恶意软件检测模型的性能。然后，通过考虑两个不同的性能参数（即准确性和F-measure）作为从实际应用中检测恶意软件的基准，对提出的恶意软件检测框架进行验证。我们对三十种不同类别的Android应用程序进行了实证研究。实验数据集包含1,94,659个良性应用程序和67个，从不同的承诺存储库中收集了538个恶意软件应用程序。实证结果表明，与所有提取的功能集相比，使用建议的功能选择框架开发的模型能够检测更多受恶意软件感染的应用程序。此外，通过使用非线性集成决策树森林（NDTF）方法构建的恶意软件检测模型实现了98.8％的检测率。除此之外，与不同的反病毒扫描程序以及文献中开发的不同框架或方法相比，所提出的恶意软件检测框架在检测受恶意软件感染的应用程序方面更为有效。利用非线性集合决策树森林（NDTF）方法构建的恶意软件检测模型，检测率达到98.8％。除此之外，与不同的反病毒扫描程序以及文献中开发的不同框架或方法相比，所提出的恶意软件检测框架在检测受恶意软件感染的应用程序方面更为有效。利用非线性集合决策树森林（NDTF）方法构建的恶意软件检测模型，检测率达到98.8％。除此之外，与不同的反病毒扫描程序以及文献中开发的不同框架或方法相比，所提出的恶意软件检测框架在检测受恶意软件感染的应用程序方面更为有效。