Several anomaly detection algorithms are available , including the SVDD , isolation forests , elliptic envelope with robust statistics , and the local outlier factor . Each algorithm makes different assumptions about (i) the defining aspects of an anomaly, (ii) the underlying distribution of the anomalies, and (iii) if the training data set (consisting of [labeled] "normal" vectors) is polluted by [mislabeled] "anomalous" vectors.