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/* * Copyright (c) 2010-2021 Haifeng Li. All rights reserved. * * Smile is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * Smile is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with Smile. If not, see . */ /** * Anomaly detection is the identification of rare items, events * or observations which raise suspicions by differing significantly from * the majority of the data. Anomalies are also referred to as outliers, * novelties, noise, deviations and exceptions. * * Three broad categories of anomaly detection techniques exist. * Unsupervised anomaly detection techniques detect anomalies in an unlabeled * test data set under the assumption that the majority of the instances * in the data set are normal by looking for instances that seem to fit * least to the remainder of the data set. Supervised anomaly detection * techniques require a data set that has been labeled as "normal" and * "abnormal" and involves training a classifier (the key difference to * many other statistical classification problems is the inherent unbalanced * nature of outlier detection). Semi-supervised anomaly detection techniques * construct a model representing normal behavior from a given normal training * data set, and then test the likelihood of a test instance to be generated * by the utilized model. *

* In particular, in the context of abuse and network intrusion detection, * the interesting objects are often not rare objects, but unexpected * bursts in activity. This pattern does not adhere to the common * statistical definition of an outlier as a rare object, and many outlier * detection methods (in particular unsupervised methods) will fail on * such data, unless it has been aggregated appropriately. Instead, a cluster * analysis algorithm may be able to detect the micro clusters formed by * these patterns. * * @author Haifeng Li */ package smile.anomaly;