Abstract
Anomaly detection in big data is a key problem in the big data analytics domain. In this paper, the definitions of anomaly detection and big data were presented. Due to the sampling and storage burden and the inadequacy of privacy protection of anomaly detection based on uncompressed data, compressive sensing theory was introduced and used in the anomaly detection algorithm. The anomaly detection criterion based on wavelet packet transform and statistic process control theory was deduced. The proposed anomaly detection technique was used for through-wall human detection to demonstrate the effectiveness. The experiments for detecting humans behind a brick wall and gypsum based on ultra-wideband radar signal were carried out. The results showed that the proposed anomaly detection algorithm could effectively detect the existence of a human being through compressed signals and uncompressed data.
Highlights
Detection refers to finding inconsistency with the desired pattern in data, which is known as novelty detection, anomaly mining, and noising mining
3.2 Experimental results and analysis In the experiments, we used the P220 UWB radar of Time Domain Company as the detection tool, and it worked in monostatic mode wherein the waveform pulses were transmitted from a single omnidirectional antenna and scattered waveforms were acquired by a collocated omnidirectional antenna
Traditional anomaly detection techniques based on complete data are confronted with many difficulties for big data
Summary
Detection refers to finding inconsistency with the desired pattern in data, which is known as novelty detection, anomaly mining, and noising mining. The low-dimensional signal contained the main features of the original signal, so CS theory can provide an effective method for anomaly detection in big data. In [7], the compressed sensing theory was used for wireless network data acquisition and achieved the anomaly detection during a signal recovery procedure based on the modified BP reconstruction algorithm.
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