Wireless sensor networks (WSNs) have received considerable attention for multiple types of applications. In particular, outlier detection in WSNs has been an area of vast interest. Outlier detection becomes even more important for the applications i
In the ¯eld of wireless sensor networks, measurements that signi¯cantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the netwo
异常检测算法综述. Outlier detection has been used for centuries to detect and, where appro- priate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument
异常值检测算法在数据挖掘的诸多领域有着应用场景，例如金融领域，信息传输领域，图像领域等。在研究过程中，有学者给出了异常点的一个定义： An outlier is an observation that deviates so much from other observations as as to arouse suspicion that it was generated by a different mechanism.
domingues-outlier-detection-evaluationdomingues-outlier-detection-evaluationNumerous machine learning methods are suitable for anomaly detection
However, supervised algorithms are more constraining than unsupervised meth-
ods as they need to be provi
In the field of wireless sensor networks, those measurements that significantly deviate from the normal patternof sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the
