MobSafe: Cloud Computing based Forensic Analysis for Massive Mobile Applications using Data Mining

With the explosive increase in Mobile apps, more and more threats migrate from traditional PC client to mobile device. Compared with traditional Win+Intel alliance in PC, Android+ARM alliance dominates in Mobile Internet, the apps replace the PC client software as the major target of malicious usage. In this paper, to improve the security status of current mobile apps, we propose a methodology to evaluate mobile apps based on Cloud Computing platform and data mining. We also present a prototype system named MobSafe to identify the mobile app’s virulence or benignancy. Compared with traditional method, such as permission pattern based method etc., MobSafe combines the dynamic and static analysis method to comprehensively evaluate a android app. In the implementation, we adopt ASEF and SAAF framework, the two representative dynamic and static analysis method, to evaluate the android apps and estimate the total time needed to evaluate all the apps stored in one mobile app market. Based on the real trace from a commercial mobile app market called AppChina, we can collect the statistics that the number of active android apps, the average number apps installed in one android device and the expanding ratio of mobile apps. As mobile app market serves as the main line of defence against mobile malwares, our evaluation results shown that it is practical to use cloud computing platform and data mining to verify all stored apps routinely to filter out malware apps from mobile app markets. As the future work, MobSafe can extensively use machine learning to conduct automotive forensic analysis of mobile apps based on the generated multifaceted