Convolutional Neural Network Based Classification of WECHAT Mini-Apps

In recent years, a novel mobile computing paradigm has been evolving rapidly, with a host app allowing users to install and run mini-apps inside the app itself. However, the current classification mechanism of mini-apps is blurry and coarse-grained, making users unable to clearly understand mini-app functions, which can result in a series of privacy issues. In this study, an automatic convolutional neural network (CNN)-based classification approach is proposed for WECHAT mini-apps. The proposed method integrates the static and dynamic features of WECHAT mini-apps to achieve precise classification. Our approach was evaluated in a real-world testbed and the results showed that it can effectively classify WECHAT mini-apps into proper categories, helping users better understand the functions of WECHAT mini-apps while reducing user privacy violations.