AR-Alarm: An Adaptive and Robust Intrusion Detection System Leveraging CSI from Commodity Wi-Fi

  • Shengjie Li
  • Xiang Li
  • Kai Niu
  • Hao Wang
  • Yue Zhang
  • Daqing ZhangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10461)


Device-free human intrusion detection holds great potential and multiple challenges for applications ranging from asset protection to elder care. In this paper, leveraging the fine-grained Channel State Information (CSI) in commodity WiFi devices, we design and implement an adaptive and robust human intrusion detection system, called AR-Alarm. By utilizing a robust feature and self-adaptive learning mechanism, AR-Alarm achieves real-time intrusion detection in different environments without calibration efforts. To further increase the system robustness, we propose a few novel methods to distinguish real human intrusion from object motion in daily life such as object dropping, curtain swinging and pets moving. As demonstrated in the experiments, AR-Alarm achieves a high detection rate and low false alarm rate.


WiFi Device-free Intrusion detection 



This work is supported by National Key Research and Development Plan under Grant No. 2016YFB1001200.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Shengjie Li
    • 1
    • 2
  • Xiang Li
    • 1
    • 2
  • Kai Niu
    • 1
    • 2
  • Hao Wang
    • 1
    • 2
  • Yue Zhang
    • 1
    • 2
  • Daqing Zhang
    • 1
    • 2
    Email author
  1. 1.Key Laboratory of High Confidence Software Technologies, Ministry of EducationBeijingChina
  2. 2.School of Electronics Engineering and Computer SciencePeking UniversityBeijingChina

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