Wi-Alarm: Low-Cost Passive Intrusion Detection Using WiFi
AbstractIn this paper, we present a WiFi-based intrusion detection system called Wi-Alarm. Motivated by our observations and analysis that raw channel state information (CSI) of WiFi is sensitive enough to monitor human motion, Wi-Alarm omits data preprocessing. The mean and variance of the amplitudes of raw CSI data are used for feature extraction. Then, a support vector machine (SVM) algorithm is applied to determine detection results. We prototype Wi-Alarm on commercial WiFi devices and evaluate it in a typical indoor scenario. Results show that Wi-Alarm reduces much computational expense without losing accuracy and robustness. Moreover, different influence factors are also discussed in this paper. View Full-Text
Share & Cite This Article
Wang, T.; Yang, D.; Zhang, S.; Wu, Y.; Xu, S. Wi-Alarm: Low-Cost Passive Intrusion Detection Using WiFi. Sensors 2019, 19, 2335.
Wang T, Yang D, Zhang S, Wu Y, Xu S. Wi-Alarm: Low-Cost Passive Intrusion Detection Using WiFi. Sensors. 2019; 19(10):2335.Chicago/Turabian Style
Wang, Tao; Yang, Dandan; Zhang, Shunqing; Wu, Yating; Xu, Shugong. 2019. "Wi-Alarm: Low-Cost Passive Intrusion Detection Using WiFi." Sensors 19, no. 10: 2335.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.