Securing Mobile Applications Against Mobile Malware Attacks: A Case Study

Abstract

Nowadays, the security exploitations against online systems and mobile applications(apps) are increasing tremendously. Due to the new norm, most of the meetings were conducted online with so many security challenges. Hence, this paper presents a new model called Mobotder to detect possible security exploitation for online meeting applications and online games based on geolocation (GPS), permissions, Application Programming Interface (API) calls, and system calls. This model was built using hybrid analysis in a controlled lab environment with the dataset from Drebin and Google Play Store for training and evaluation. As proof of concept (POC) for the developed model, a case study consists of twenty (20) online meeting applications were conducted. As a result, 10% of the tested mobile apps were at high risk of potentially being exploited by the attackers. While for online games, 7 out of 10 anonymous evaluated online games were identified as medium risk. As for future work, this model can be used as the benchmark and guideline in developing a mobile malware detection system for online mobile apps.