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Detecting Evil-Twin Attack with the Crowd Sensing of Landmark in Physical Layer

  • Chundong Wang
  • Likun Zhu
  • Liangyi Gong
  • Zheli Liu
  • Xiuliang Mo
  • Wenjun Yang
  • Min Li
  • Zhaoyang Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11337)

Abstract

With the popularity of mobile computing, WiFi has become one of the essential technologies for people to access the Internet, and WiFi security has also become a major threat for mobile computing. The Evil-Twin attack can steal a large amount of private data by forging the same SSID as the real Access Point. This paper proposes a passive Evil-Twin attack detection scheme through CSI in physical layer. First of all, we propose a location model based on the edge of landmark area. In this model, the improved MUSIC algorithm is used to calculate each AP’s AoA by CSI phase. Secondly, it proposes an algorithm for simplifying the generation of location model files, which is the dataset of a small number of AoA and RSSI samples. Finally, according to location model, attack detection algorithm combines a large number of crowd sensing data to determine whether it is a malicious AP. Experiments show that our attack detection system achieves a higher detection rate.

Notes

Acknowledgments

Our work was supported by the Foundation of the Educational Commission of Tianjin, China (Grant No.2013080), the General Project of Tianjin Municipal Science and Technology Commission under Grant (No.15JCYBJC15600), the Major Project of Tianjin Municipal Science and Technology Commission under Grant (No. 15ZXDSGX00030), and NSFC: The United Foundation of General Technology and Fundamental Research (No. U1536122). The authors would like to give thanks to all the pioneers in this field, and also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Chundong Wang
    • 1
    • 2
  • Likun Zhu
    • 1
    • 2
  • Liangyi Gong
    • 1
    • 2
  • Zheli Liu
    • 3
  • Xiuliang Mo
    • 1
    • 2
  • Wenjun Yang
    • 1
    • 2
  • Min Li
    • 3
  • Zhaoyang Li
    • 3
  1. 1.Key Laboratory of Computer Vision and System, Ministry of EducationTianjin University of TechnologyTianjinChina
  2. 2.Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Ministry of EducationTianjin University of TechnologyTianjinChina
  3. 3.Nankai UniversityTianjinChina

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