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

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Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2018)

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.

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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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Authors and Affiliations

  1. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Zhenyu Cheng, Xunxun Chen, Yongzheng Zhang, Shuhao Li & Jian Xu

  2. School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

    Zhenyu Cheng, Xunxun Chen, Yongzheng Zhang, Shuhao Li & Jian Xu

Authors
  1. Zhenyu Cheng
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  2. Xunxun Chen
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  3. Yongzheng Zhang
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  4. Shuhao Li
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  5. Jian Xu
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Corresponding author

Correspondence to Xunxun Chen .

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Editors and Affiliations

  1. Shanghai University, Shanghai, China

    Honghao Gao

  2. University of West London, London, UK

    Xinheng Wang

  3. Hangzhou Dianzi University, Hangzhou Shi, Zhejiang, China

    Yuyu Yin

  4. London South Bank University, London, UK

    Muddesar Iqbal

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Cheng, Z., Chen, X., Zhang, Y., Li, S., Xu, J. (2019). MUI-defender: CNN-Driven, Network Flow-Based Information Theft Detection for Mobile Users. In: Gao, H., Wang, X., Yin, Y., Iqbal, M. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 268. Springer, Cham. https://doi.org/10.1007/978-3-030-12981-1_23

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  • DOI: https://doi.org/10.1007/978-3-030-12981-1_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-12980-4

  • Online ISBN: 978-3-030-12981-1

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