Mobile Malware Classification for Social Media Application

Abstract

Organisations and users face many challenges against smartphone in detecting mobile malware attacks. Many techniques have been developed by different solution providers to ensure that smartphones remain free from such attacks. Nonetheless, we still lack efficient techniques to detect mobile malware attacks, especially for the social media application. Hence, this paper presents mobile malware classifications based on API and permission that can be used for mobile malware detection with regard to the social media applications. A mobile malware classification based on correlation of malware behaviour, vulnerability exploitation and mobile phone has been developed for this purpose and a mobile application (app) has been sought to support this new classification. This research was conducted in a controlled lab environment using open source tools and by applying hybrid analysis. Based on the testing conducted, the results showed that the mobile apps were categorized as dangerous with 16% for call log exploitation, 13% for audio exploitation and 9% for GPS exploitation. These results indicated that the attackers could launch possible different cyber attacks. In future, this paper can be used as reference for other researchers with the same interest.