Abstract
In this paper, we revisit the simple probabilistic approach of unsupervised anomaly detection by estimating multivariate probability as a product of univariate probabilities, assuming attributes are generated independently. We show that this simple traditional approach performs competitively to or better than five state-of-the-art unsupervised anomaly detection methods across a wide range of data sets from categorical, numeric or mixed domains. It is arguably the fastest anomaly detector. It is one order of magnitude faster than the fastest state-of-the-art method in high dimensional data sets.
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Notes
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Though \(\hat{P}(\cdot )\) can be estimated directly in numeric domains, it is a lot easier and faster to do it in categorical domains.
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Acknowledgments
We would like to thank Prof. Takashi Washio for providing very useful comments and suggestions. We are thankful to the anonymous reviewers for their critical comments to improve the quality of the paper.
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Aryal, S., Ting, K.M., Haffari, G. (2016). Revisiting Attribute Independence Assumption in Probabilistic Unsupervised Anomaly Detection. In: Chau, M., Wang, G., Chen, H. (eds) Intelligence and Security Informatics. PAISI 2016. Lecture Notes in Computer Science(), vol 9650. Springer, Cham. https://doi.org/10.1007/978-3-319-31863-9_6
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DOI: https://doi.org/10.1007/978-3-319-31863-9_6
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