Invasion detection method based on channel state information and support vector machine

Abstract

The invention provides an invasion detection method based on channel state information and a support vector machine. No special hardware facility is needed, an existing wireless network is fully used, and a common business router is used to realize security monitoring function. The coverage scope is wide, and privacy exposure can be prevented. The invasion detection method includes the steps of after obtaining CSI original data, conducting clustering and de noising for the subcarrier data in a channel by using a density-based clustering algorithm DBSCAN, smoothing the denoised data by using weight-based sliding average algorithm, and extracting characteristic values for data by using major constituent analyzing algorithm after data pre-processing. Data subjected to pretreatment and feature extraction can more accurately reflect the main change of signals and greatly reduce number of dimensions. The invasion detection precision is improved and the calculating complexity is reduced. The method uses an SVM classification algorithm to obtain a statistics model of non-linear dependence relation between an invasion state and a signal fingerprint, thereby achieving the purpose of invasion detection.

Description

technical field [0001] The invention relates to the field of indoor intrusion detection, in particular to an identification method for intrusion detection based on channel state information and using support vector machine technology. Background technique [0002] Wi-Fi-based wireless local area networks are widely deployed indoors, providing intrusion detection services as well as data transmission services. When protecting some property or monitoring important areas, people often use some vision-based devices, such as cameras, or infrared-based sensors. Although these devices work well in certain specific environments, there are often many limitations. A fatal shortcoming of vision-based devices is that the monitoring must be within the range of a straight line of sight. Once there is an occluder, the device cannot effectively guarantee normal security, and vision-based devices are easy to expose private content. Some sensitive areas cannot be deployed. Although infrare...

AI Technical Summary

Problems solved by technology

RSSI is currently the most widely used energy characteristic, but its coarse-grainedness and variability are not suitable for accurate perception in multipath indoor environments, and the accuracy of intrusion detection is very poor

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