Convolutional Neural Network Based Classification of WeChat Mini-Apps

Authors

Yihao Jin, Nanjing University
Xuanyu Liu, Nanjing University
Xiao Fu, Nanjing University
Bin Luo, Nanjing University
Xiaojiang Du, Stevens Institute of Technology
Mohsen Guizani, Mohamed Bin Zayed University of Artificial IntelligenceFollow

Document Type

Conference Proceeding

Publication Title

IEEE International Conference on Communications

Abstract

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 Wechatmini-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 Wechatmini-apps into proper categories, helping users better understand the functions of WeChat mini-apps while reducing user privacy violations.

First Page

747

Last Page

752

DOI

10.1109/ICC45041.2023.10279007

Publication Date

10-23-2023

Keywords

Training, Privacy, Social networking (online), Redundancy, Static analysis, Message services, Feature extraction, CNN, Mini-app classification, Mobile computing, Wechat

Comments

IR conditions: non-described

Recommended Citation

Y. Jin, X. Liu, X. Fu, B. Luo, X. Du and M. Guizani, "Convolutional Neural Network Based Classification of WeChat Mini-Apps," ICC 2023 - IEEE International Conference on Communications, Rome, Italy, 2023, pp. 747-752, doi: 10.1109/ICC45041.2023.10279007.