@inproceedings{40fc89624b4f4ae5982b64ea3b7d558b,
title = "AR-Alarm: An adaptive and robust intrusion detection system leveraging CSI from commodity Wi-Fi",
abstract = "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.",
keywords = "Device-free, Intrusion detection, WiFi",
author = "Shengjie Li and Xiang Li and Kai Niu and Hao Wang and Yue Zhang and Daqing Zhang",
note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 15th International Conference on Smart Homes and Health Telematics, ICOST 2017 ; Conference date: 29-08-2017 Through 31-08-2017",
year = "2017",
month = jan,
day = "1",
doi = "10.1007/978-3-319-66188-9\_18",
language = "English",
isbn = "9783319661872",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "211--223",
editor = "Bessam Abdulrazak and Hamdi Aloulou and Mounir Mokhtari",
booktitle = "Enhanced Quality of Life and Smart Living - 15th International Conference, ICOST 2017, Proceedings",
}