Android mobile malware detection using fuzzy AHP

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.

Automated summary

The study proposed a multi-criteria decision-making based mobile malware detection system using risk analysis approach to evaluate Android applications, aiming to increase user awareness of permission requests, with a high accuracy rate achieved.