In Wireless Sensor Networks (WSNs), abnormal sensing data is easily generated due to factors such as the harsh working environment, sensor faults and external events. In order to enhance the detection rate of abnormal data and reduce the false positive rate, we propose a distributed anomaly detection method using one-class quarter-sphere support vector machine (QSSVM) based on temporal-spatial fusion in WSNs. Firstly, according to the synthetic data, the temporal-spatial QSSVM model is trained to determine the relevant parameters. Secondly, the trained QSSVM model is used to classify the streaming data in WSNs, and the abnormal data types are classified into noise, faults and events. Finally, the method decides whether to update the classification model based on whether the new sample has an effect on the boundary of the hypersphere. The experimental results illustrate that the proposed method has a detection rate of 96% compared with other three methods, and the false positive rate is only 14%.
This work was supported by Natural Science Foundation of China (61471067, 61571051) and Beijing Natural Science Foundation (4172024, 4172026).