# Robust Device-Free Intrusion Detection Using Physical Layer Information of WiFi Signals
## 2 Related Work  
WiFi-based passive human detection is the fundamental technique of various ubiquitous wireless sensing applications, such as indoor localization, human identification and activity recognition. It can be widely deployed in smart home scenarios to ensure the security. A large quantity of studies about wireless sensing promote the development of wireless sensing. Earlier passive human detection systems usually utilize RSSI from the MAC layer of the wireless network. After [PERSON] et al. proposed the concept of device-free passive human motion detection, they optimized their approach and made the system work in real environments [10]. [PERSON] leveraged probabilistic techniques, and had the capability to both localize a single entity and estimate the number of people in the area of interest [15]. Since RSSI is a coarse-grained measurement of wireless networks, many RSSI-based human detection systems deployed multiple pairs of transceivers to achieve a higher accuracy [16]. Another technique of human detection using multiple pairs of transceivers is Radio Tomographic Imaging (RTI) [17]. Researchers also developed various approaches based on RTI, such as the kRTI [18] and dRTI [19]. However, RSSI-based human detection systems suffer from severe multi-path efficiency [20]. As a result, more and more researchers move their attention to the more fine-grained measurement, CSI. To overcome the shortcomings of RSSI-based human detection systems, Fine-grained device-free Motion Detection (FIMD) utilized the burst pattern of CSIs during human motion to detection human presence [21]. Fine-grained Indoor Localization (FILA) explored the frequency diversity of the subcarriers in Orthogonal Frequency Division Multiplexing (OFDM) systems, and constructed a signal propagation model [22; 23]. As human motion can cause the fluctuation of the signal, Bfp harnessed the variance of the amplitude of the CSIs to improve the performance of human detection [11]. PADS took advantages of the whole information of CSI including both amplitude and phase feature to detect human motion with various speeds [24]. It calculates the maximum eigenvalue of covariance matrix of  
Figure 1: Different moving patterns in intrusion scenarios. (a) Regular walking; (b) walking while bending down; and (c) creeping. normalized amplitude and phase information, respectively, as the feature. Support Vector Machine (SVM) is used as the classifier. FRID explored the phase feature of CSIs and achieved calibration-free human detection without the need of a normal profile [25; 26]. Short-term averaged variance ratio (SVR) and long-term averaged variance ratio which are two schemes based on the coefficient of variance of phase are introduced to eliminate the re-calibration cost. Conventional human detection systems demonstrated directional monitoring coverage, and [PERSON] et al. utilized CSI features to virtually tune the coverage shape into disk-like [27]. Speed Independent Entity Detection (SIED) extracted a novel feature from the whole wireless channel and transformed human detection into a probabilistic problem to achieve a high detection accuracy [5]. AR-Alarm utilized a self-adaptive learning mechanism to achieve intrusion detection without the need of re-calibration [13]. Besides human detection, wireless signals can be used in indoor localization, activity recognition and even human identification. [PERSON] et al. utilized CSI to provide a localization approach with a high accuracy leveraging only a single pair of transceiver [28]. CSI-MIMO utilized frequency diversity of CSI to construct the fingerprint of different locations and achieved a localization accuracy of 0.95 m [29]. SpotFi computed the Angle of Arrival (AoA) of multipath components of different antennas and improved the localization accuracy to 40 cm [30]. HiDFPL proposed a measurement to represent the sensitivity of the receiver and enhanced the localization accuracy [31]. [PERSON] et al.