Codar: Complex-valued Neural Network for Crossing-Floor Intrusion Detection via WiFi

Abstract

WiFi systems offer enormous potential for device-free human intrusion detection. Current methods often require routers to be deployed in multiple adjacent rooms on the same floor, which is redundant and costly. To solve this, we introduce the first work on intrusion detection in the crossing-floor scenario via WiFi. Routers on different floors are utilized without major modifications to the existing router layout. Many previous works require a high sample rate and ignore the phase information. In this paper, we propose Codar, a complex-valued LSTM-CNN neural network. The LSTM effectively captures temporal dependencies at a low sample rate in harsh propagation environments. Moreover, amplitude and phase features are explored jointly by complex-valued operations. Experimental results demonstrate Codar achieves 95%, 94.5%, and 99% accuracy for intrusion detection, user identification, and intruded floor identification, surpassing competitive methods. The code and dataset are available at https://github.com/ouweiting/Codar.