Convolutional Neural Network Based Classification of WeChat Mini-Apps

Yihao Jin, Xuanyu Liu, Xiao Fu, Bin Luo, Xiaojiang Du, Mohsen Guizani

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationICC 2023 - IEEE International Conference on Communications
Subtitle of host publicationSustainable Communications for Renaissance
EditorsMichele Zorzi, Meixia Tao, Walid Saad
Pages747-752
Number of pages6
ISBN (Electronic)9781538674628
DOIs
StatePublished - 2023
Event2023 IEEE International Conference on Communications, ICC 2023 - Rome, Italy
Duration: 28 May 20231 Jun 2023

Publication series

NameIEEE International Conference on Communications
Volume2023-May
ISSN (Print)1550-3607

Conference

Conference2023 IEEE International Conference on Communications, ICC 2023
Country/TerritoryItaly
CityRome
Period28/05/231/06/23

Keywords

  • CNN
  • Wechat
  • mini-app classification
  • mobile computing

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