Wireless Sensor Network Anomaly Detection of QSSVM Model Based on DBN
Wireless Sensor Network(WSN)data exception processing efficiency guarantees the high efficiency of intelligent operation.On the basis of analyzing the test model of Quarter-Sphere support vector machines(QSS-VM),the Deep Belief Network(DBN)is constructed and implemented.This paper designs an anomaly detection algorithm which can realize online test function.The results show that the computing time increases with the in-crease of window size.QSSVM changes as the window begins to expand,mainly in the form of continuous improve-ment in accuracy.The detection performance of QSSVM is greatly improved with the continuous increase of sample dimensions,while the detection performance of K-means has a decreasing trend.When QSSVM algorithm is used to process 560 dimensional HAR data,the test results show that the detection rate is as high as 94.16%.The re-search can meet the data processing requirements of large-scale high dimensional sensors and has high application value.
sensor networkabnormal datadeep belief networkhyperspherical support vector machine
NETL
NSTL
万方数据
维普
