Elevating Indoor Security with Detection Through the Walls

Indoor intrusion detection systems (IDS) are crucial for securing residential and commercial spaces. However, existing solutions often require expensive, professional installations or suffer from high false alarm rates (FARs) due to non-human movements. We introduce the first indoor IDS that leverages ubiquitous WiFi signals to detect intrusions through walls while remaining robust against interference from non-human subjects. It comprises three key components: robust signal pre-processing to extract environment-independent statistics; a ResNet model for motion source identification; and an LSTM-based state machine to incorporate historical data. Notably, our system operates independently of device location, subject orientation, and environmental changes, enabling swift deployment in real-world environments. Implemented with single-pair commodity WiFi devices, it was extensively evaluated in two typical indoor settings with various interference sources such as pets, cleaning robots, and fans. Results demonstrate that our system achieves an intrusion detection accuracy of 96.67% with a false alarm rate of 1.92%. These findings highlight its robustness and significant potential for widespread deployment in indoor security applications.