With the recognition of free apps, Android has become the most widely used smartphone operating system these days and it naturally invited cyber-criminals to build malware-infected apps that can steal vital information from these devices. The most critical problem is to detect malware-infected apps and keep them out of Google play store. The vulnerability lies in the underlying permission model of Android apps. Consequently, it has become the responsibility of the app developers to precisely specify the permissions which are going to be demanded by the apps during their installation and execution time. In this study, we examine the permission-induced risk which begins by giving unnecessary permissions to these Android apps. The experimental work done in this research paper includes the development of an effective malware detection system which helps to determine and investigate the detective influence of numerous well-known and broadly used set of features for malware detection. To select best features from our collected features data set we implement ten distinct feature selection approaches. Further, we developed the malware detection model by utilizing LSSVM (Least Square Support Vector Machine) learning approach connected through three distinct kernel functions i.e., linear, radial basis and polynomial. Experiments were performed by using 2,00,000 distinct Android apps. Empirical result reveals that the model build by utilizing LSSVM with RBF (i.e., radial basis kernel function) named as FSdroid is able to detect 98.8% of malware when compared to distinct anti-virus scanners and also achieved 3% higher detection rate when compared to different frameworks or approaches proposed in the literature.

随着免费应用程序的普及，Android 已成为当今使用最广泛的智能手机操作系统，它自然会邀请网络犯罪分子构建受恶意软件感染的应用程序，这些应用程序可以从这些设备中窃取重要信息。最关键的问题是检测受恶意软件感染的应用程序并将它们排除在 Google Play 商店之外。该漏洞存在于 Android 应用程序的底层权限模型中。因此，应用程序开发人员有责任精确指定应用程序在安装和执行期间将要求的权限。在这项研究中，我们检查了权限引发的风险，首先是向这些 Android 应用程序授予不必要的权限。本研究论文中完成的实验工作包括开发有效的恶意软件检测系统，该系统有助于确定和调查众多知名且广泛使用的恶意软件检测功能集的检测影响。为了从我们收集的特征数据集中选择最佳特征，我们实施了十种不同的特征选择方法。此外，我们利用 LSSVM（最小二乘支持向量机）学习方法开发了恶意软件检测模型，该方法通过三个不同的核函数连接，即线性、径向基和多项式。使用 2,00,000 个不同的 Android 应用程序进行了实验。经验结果表明，利用名为FSdroid的带有RBF（即径向基核函数）的LSSVM构建的模型能够检测到98。