Roland Molontay : Copula-Based Anomaly Scoring of High-Dimensional Data with Application in Telecommunication Networks

Posted by Kyunghee Han , part of the Statistics and Data Science Seminar.

At

Oct. 19, 2022, 4 p.m.

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Abstract

Anomaly detection refers to the process of identifying unexpected objects or patterns, which do not conform to the usual behavior. The detection of “not-normal” observations has attracted a lot of research interest from the machine learning community since it has a wide variety of practical applications. In this talk, I will briefly present an overview of the challenges of unsupervised anomaly detection. I will also present our novel model-based approach that relies on the multivariate probability distribution associated with the observations [1]. 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 and also with missing values. I will also 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. [1]: Horváth, G., Kovács, E., Molontay, R., & Nováczki, S. (2020). Copula-based anomaly scoring and localization for large-scale, high-dimensional continuous data. ACM Transactions on Intelligent Systems and Technology (TIST), 11(3), 1-26