Leveraging ubiquitous WiFi infrastructures, intrusion detection methods based on Received Signal Strength (RSS) offer compelling advantages, including cost-effectiveness and privacy protection. However, existing RSS-based intrusion detection solutions fall short of accurately estimating and extending the WiFi sensing bound. In this paper, we propose a novel model of motion-disturbed RSS and design an effective R-ratio indicator to extend the intrusion detection bound. Specifically, we first establish a general model of motion-disturbed RSS and derive the blocked area and reflection area in this RSS model. Then, we define the WiFi intrusion detection bound and propose a performance indicator called R-ratio to extend the bound with RSS. Furthermore, based on the statistical properties of noise, we design an efficient filter to further weaken the noise. We also propose two new methods to further extend intrusion detection bound. Extensive experimental results demonstrate that the proposed power sum ratio based intrusion detection method can approximately double the WiFi intrusion detection bound compared to other methods with raw RSS data, and our developed motion-disturbed RSS model can provide valuable insights and guidance to the intrusion detection system.
