RoFi: Robust WiFi Intrusion Detection via Distribution Matching

Intrusion detection acts as a key to in-home security, where WiFi-based systems have gained wide attention due to the ubiquitous nature of WiFi signals. While existing methods achieve impressive performance in specific environments, they are susceptible to environmental changes, especially for complex scenarios where outdoor human activities can be mistaken as intrusions. In this paper, we propose RoFi, a robust WiFi intrusion detection system which can handle more complex scenarios. It achieves this by exploring the distribution of autocorrelation function (ACF) of Channel State Information (CSI) when intrusion occurs, where likelihood ratio testing is employed to discriminate intrusion and non-intrusion scenarios, eliminating the variance of different environments. Without complex calibration, RoFi achieves an accuracy of over 97.5% in practical deployment, outperforming existing methods.