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MUI-defender: CNN-Driven, Network Flow-Based Information Theft Detection for Mobile Users

  • Zhenyu Cheng
  • Xunxun ChenEmail author
  • Yongzheng Zhang
  • Shuhao Li
  • Jian Xu
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 268)

Abstract

Nowadays people save a lot of privacy information in mobile devices. These information can be theft by adversaries through suspicious apps installed in smartphones, and protecting users’ privacy has become a great challenge. So developing a method to identify if there are apps thieving users’ personal information in smartphones is important and necessary. Through the analysis of apps’ network traffic data, we observe that general apps generate regular network flows with the users’ normal operations. But information theft apps’ network flows have no relationship with users’ operations. In this paper we propose a model MUI-defender (Mobile Users’ Information defender), which is based on analyzing the relationship between users’ operation patterns and network flows with CNN (Convolutional Neural Network), can efficiently detect information theft. Because of C&C (Command-and-Control) server invalidation [33] and system version incompatibility [25], etc., most of the collected information theft apps can’t run properly in reality. So we extract information theft code modules from some of these apps, and then recode and compile them into the ITM-capsule (Information Theft Modules capsule) for verification. Finally, we run the ITM-capsule and several normal apps to detect the network flows, which shows our detection model can achieve an accuracy higher than 94%. Therefore, MUI-defender is suitable for detecting the network flows of information theft.

Keywords

Information theft Network flow Operation pattern CNN 

Notes

Acknowledgement

This work was supported by the National Key Research and Development Program of China (No. 2016YFB0801502), and the National Natural Science Foundation of China (Grant No. U1736218). We are grateful for the assistance from the volunteers. Thanks to their valuable contribution to the experiments in this paper. We also want to thank the reviewers for the thorough comments and helpful suggestions.

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Zhenyu Cheng
    • 1
    • 2
  • Xunxun Chen
    • 1
    • 2
    Email author
  • Yongzheng Zhang
    • 1
    • 2
  • Shuhao Li
    • 1
    • 2
  • Jian Xu
    • 1
    • 2
  1. 1.Institute of Information EngineeringChinese Academy of SciencesBeijingChina
  2. 2.School of Cyber SecurityUniversity of Chinese Academy of SciencesBeijingChina

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