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The latest developments in anomaly detection algorithms are based on models that describe what is a “normal” behavior (i.e., expected distribution of the data), and try to identify exceptions or outliers in the data. These models are usually based on classic distributions, such as Gaussian distribution or Mahalanobis distances that can be detected using corresponding AI optimization solutions such as Principal component analysis (PCA) or mixture of Gaussians, respectively. Modern solutions may also include deep learning for detecting the outliers.