# Robust Device-Free Intrusion Detection Using Physical Layer Information of WiFi Signals
## 1 Introduction  
Device-free human detection has attracted a lot of interest in recent years. It can detect human presence in the monitoring area without any sensing-related devices attached to the people [1]. It can be used well in intrusion detection systems, which is a vital security component in a smart home. Aiming at handling the security issues in a smart home, many techniques have been utilized to implement device-free human detection, such as video-based, infrared-based, Radio Frequency Identification (RFID)-based and Ultra-Wide Bandwidth (UWB)-based approaches. Although they have a good detection accuracy, these approaches have limited using conditions and need dedicated devices that hinder their adoption. WiFi-enabled devices become the catalyst of device-free sensing as they have been widely deployed in both public and private buildings. Besides being used for communication, WiFi networks can also be used as sensor networks [2; 3; 4]. Many applications have emerged based on WiFi infrastructures, human detection [5], indoor localization [6], and even human identification [7] are some representative applications. A typical WiFi-based device-free human detection system usually contains several pairs of transmitters and receivers. A wireless router can act as a transmitter, while a WiFi-enabled device can act as a receiver. As a result, it doesn't have the problem of key management [8; 9] compared with sensor-based approaches. The rational of WiFi-based device-free human detection is that human presence has an impact on signal propagation, which will cause the signal strength fluctuation at the receiver [10]. Previous WiFi-based human detection systems utilize Received Signal Strength Indicator (RSSI) from Media Access Control (MAC) layer for it is easy to obtain. However, RSSI is a coarse-grained measurement. In the typical indoor scenario, RSSI becomes unreliable due to multipath fading. It may increase, decrease, or even remain the same when a person moves in the monitoring area. Recently, many studies explore CSI from physical layer of wireless networks to detect human motion [11; 12; 13]. As indicated in [14], CSI is a subcarrier-level measurement that is more fine-grained compared with RSSI. It is more sensitive to environmental changes while keeps quite stable in static scenarios. As a result, CSI succeeds in improving the performance of human detection. However, state-of-the-art human detection techniques still have limitations for intrusion detection systems. Common human detection techniques can only detect a human who is walking with a regular pattern. Nevertheless, an intruder in the building is likely to keep away from the security devices or move very slowly to hide himself from being monitored. Furthermore, most human detection techniques require on-site calibration of both static and dynamic environments. On-site calibration is labor intensive and it needs professional deployment and maintenance that makes a human detection system more complex in practical use. Consequently, human detection techniques will fail in detecting intruders in security systems, and we need to explore effective features to model human motion. To deal with the limitations, in this work, we propose a Robust Device-Free Intrusion Detection (RDFID) system leveraging fine-grained CSI. We investigate the impact of human motion on WiFi signals and demonstrate that different patterns of human motion in different scenarios can be modeled by a unified framework. First, we extract the wavelet variance of CSIs from frequency domain as the feature. It is more sensitive to human motion, and more robust under different moving patterns. In addition, the feature values of static and intrusion can be seen to be generated by different Gaussian Models. As a result, intrusion can be detected using a Gaussian Mixture Model (GMM). As shown in Figure 1, RDFID can detect human motion of different moving patterns.