Android mobile is very challenging because it is an open-source operating system that is also vulnerable to attacks. Previous studies have shown various mobile malware detection methods to overcome this problem, but still, there is room for improvement. Mobile users mostly ignore long lists of permissions because these are difficult to understand. Therefore, to distinguish benign or malware applications and the probability of each permission request is understood, it is necessary to evaluate Android mobile applications. This research proposed a multi-criteria decision-making based (MCDM) mobile malware detection system using a risk-based fuzzy analytical hierarchy process (AHP) approach to evaluate the Android mobile application. This study focuses on static analysis, that uses permission-based features to assess the mobile malware detection system approach. Risk analysis is applied to increase the awareness of the mobile user in granting any permission request to contain a high-risk level. The evaluation used 10,000 samples taken from Drebin and AndroZoo. The results show a high accuracy rate of 90.54% values that can effectively classify the Android application into four different risk levels.

Android 手机非常具有挑战性，因为它是一个开源操作系统，也容易受到攻击。先前的研究表明，各种移动恶意软件检测方法可以克服这个问题，但仍有改进的余地。移动用户大多会忽略一长串权限，因为它们很难理解。因此，要区分良性或恶意应用程序并了解每个权限请求的概率，就需要对Android 移动应用程序进行评估。本研究提出了一种基于多标准决策 (MCDM) 的移动恶意软件检测系统，使用基于风险的模糊层次分析法 (AHP) 方法来评估 Android 移动应用程序。本研究侧重于静态分析，它使用基于权限的功能来评估移动恶意软件检测系统方法。应用风险分析以提高移动用户在授予任何包含高风险级别的权限请求时的意识。评估使用了来自 Drebin 和 AndroZoo 的 10,000 个样本。结果显示 90.54% 的高准确率值，可以有效地将 Android 应用程序分为四个不同的风险级别。