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, MobSafe combines the dynamic and static analysis methods to comprehensively evaluate an Android app. In the implementation, we adopt Android Security Evaluation Framework （ASEF） and Static Android Analysis Framework （SAAF）, the two representative dynamic and static analysis methods, 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 of 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 show 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 data in this stage.

A Forensic Method for Efficient File Extraction in HDFS Based on Three-Level Mapping

The large scale and distribution of cloud computing storage have become the major challenges in cloud forensics for file extraction. Current disk forensic methods do not adapt to cloud computing well and the forensic research on distributed file system is inadequate. To address the forensic problems, this paper uses the Hadoop distributed file system （HDFS） as a case study and proposes a forensic method for efficient file extraction based on three-level （3L） mapping. First, HDFS is analyzed from overall architecture to local file system. Second, the 3L mapping of an HDFS file from HDFS namespace to data blocks on local file system is established and a recovery method for deleted files based on 3L mapping is presented. Third, a multi-node Hadoop framework via Xen virtualization platform is set up to test the performance of the method. The results indicate that the proposed method could succeed in efficient location of large files stored across data nodes, make selective image of disk data and get high recovery rate of deleted files.