Density level set estimation, also known as one-class learning (Tax and Duin, 1999;Schölkopf et al., 1999), is the problem of learning a representation of a single class of interest, rejecting data points that deviate from the learned model of normality.
it has been proven very successful in anomaly detection scenarios such as network intrusion detection (Görnitz et al., 2009).