Robust Device-Free Intrusion Detection Using Physical Layer Information of WiFi Signals

Abstract

WiFi infrastructures are widely deployed in both public and private buildings. They make the connection to the internet more convenient. Recently, researchers find that WiFi signals have the ability to sense the changes in the environment that can detect human motion and even identify human activities and his identity in a device-free manner, and has many potential security applications in a smart home. Previous human detection systems can only detect human motion of regular moving patterns. However, they may have a significant detection performance degradation when used in intrusion detection. In this study, we propose Robust Device-Free Intrusion Detection (RDFID) system leveraging fine-grained Channel State Information (CSI). The noises in the signals are removed by a Principle Component Analysis (PCA) and a low pass filter. We extract a robust feature of frequency domain utilizing Continuous Wavelet Transform (CWT) from all subcarriers. RDFID captures the changes from the whole wireless channel, and a threshold is obtained self-adaptively, which is calibration-free in different environments, and can be deployed in smart home scenarios. We implement RDFID using commodity WiFi devices and evaluate it in three typical office rooms with different moving patterns. The results show that our system can accurately detect intrusion of different moving patterns and different environments without re-calibration.