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Copula-Based Anomaly Scoring and Localization for Large-Scale, High-Dimensional Continuous Data
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DOI：10.1145/3372274.png)
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摘要
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The anomaly detection method presented by this article has a special feature: it not only indicates whether or not an observation is anomalous but also tells what exactly makes an anomalous observation unusual. Hence, it provides support to localize the reason of the anomaly. The proposed approach is model based; it relies on the multivariate probability distribution associated with the observations. Since the rare events are present in the tails of the probability distributions, we use copula functions, which are able to model the fat-tailed distributions well. The presented procedure scales well; it can cope with a large number of high-dimensional samples. Furthermore, our procedure can cope with missing values as well, which occur frequently in high-dimensional datasets. In the second part of the article, we demonstrate the usability of the method through a case study, where we analyze a large dataset consisting of the performance counters of a real mobile telecommunication network. Since such networks are complex systems, the signs of sub-optimal operation can remain hidden for a potentially long time. With the proposed procedure, many such hidden issues can be isolated and indicated to the network operator.
Keyword:
Anomaly scoring
unsupervised learning
copula fitting
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