Research on the Mixed Teaching Model in Colleges and Universities in the Context of"Internet+"

Under the tide of"Internet+education",the new means of information technology education is constantly used in education and teaching,and the traditional teaching models and teaching methods urgently need to be changed.There is no doubt about the integration of online teaching and offline teaching.On the basis of reviewing the research on the mixed teaching model at home and abroad,this paper puts forward the idea of constructing the mixed teaching model in colleges and universities in the context of"Internet+",and sets up the mixed teaching model in colleges and universities in the context of"Internet+".Through the detailed and concrete design of the operation process of the mixed teaching model,we can break the single shackles of online teaching and face-to-face teaching,and form an interactive and collaborative teaching ecosystem to realize the common development of online teaching and offline teaching.

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.