Abstract
In this paper, we present a WiFi-based intrusion detection system called Wi-Alarm. Motivated by our observations and analysis that raw channel state information (CSI) of WiFi is sensitive enough to monitor human motion, Wi-Alarm omits data preprocessing. The mean and variance of the amplitudes of raw CSI data are used for feature extraction. Then, a support vector machine (SVM) algorithm is applied to determine detection results. We prototype Wi-Alarm on commercial WiFi devices and evaluate it in a typical indoor scenario. Results show that Wi-Alarm reduces much computational expense without losing accuracy and robustness. Moreover, different influence factors are also discussed in this paper.
Highlights
Intrusion detection is a dynamic process of monitoring whether there exists any entity breaking into a given area, making alarms if necessary
WiFi-based systems can be divided into device-based active approaches and device-free passive (DFP) [4] approaches
Motivated by our experiment observations that, compared with static environment, human motion leads to higher channel state information (CSI) amplitudes with greater variances, we proposed the mean and variance of CSI amplitude to be a couple of good indicators for intrusion detection
Summary
Intrusion detection is a dynamic process of monitoring whether there exists any entity breaking into a given area, making alarms if necessary. It gains more and more attention and has great potential in many applications, such as border protection [1], smart home [2], elderly health care [3]. WiFi-based systems can be divided into device-based active approaches and device-free passive (DFP) [4] approaches The former requires a target to carry a mobile device and turn on its WiFi, the target is identified by that device.
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