工业的网络安全直接影响到经济效益,无线传感器网络(Wireless Sensor Network,WSN)异常数据处理显得尤为重要.设计了一种基于1/4超球面支持向量机(Quarter-Sphere Support Vector Machines,QSSVM)方法的工业无线传感器网络异常数据检测方法,实现在线测试功能的异常检测算法.研究结果表明:增大视窗尺寸时,所需的运算时间也明显增加,算法精度也发生了连续提升;检测能力则会随采样维数的增加而发生大幅提升;相对K-means算法,QSSVM检测率明显增加,可以达到99.5%以上.该研究能够满足工业大量数据处理需求,具有很高的应用价值.
Abnormal Data Detection and Analysis of Industrial Wireless Sensor Networks Based on QSSVM
The industrial Network security directly affects the economic benefits,Wireless Sensor Network(WSN)abnormal data processing is particularly important.Therefore,an anomaly detection method based on Quarter-Sphere support vector machines(QSSVM)is designed to realize the anomaly detection algorithm of online testing function in industrial wireless sensor networks.The results show that when the window size is increased,the operation time is obviously increased,and the algorithm accuracy is continuously improved.The detection capability will be greatly improved with the increase of sampling dimension.Compared with K-means algorithm,the detection rate of QSSVM is significantly increased,which can reach more than 99.5%.This research can meet the needs of large amount of data processing in industry and has high application value.