Categorical anomaly detection in heterogeneous data using minimum description length clustering

14 Jun 2020James CheneyXavier GombauGhita BerradaSidahmed Benabderrahmane

Fast and effective unsupervised anomaly detection algorithms have been proposed for categorical data based on the minimum description length (MDL) principle. However, they can be ineffective when detecting anomalies in heterogeneous datasets representing a mixture of different sources, such as security scenarios in which system and user processes have distinct behavior patterns... (read more)

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