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| Identifier |
000429153 |
| Title |
Benchmarking anomaly detectors on streaming data |
| Alternative Title |
Πειραματική αξιολόγηση ανιχνευτών ανωμαλιών σε ροές δεδομένων |
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Author
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Γιαννούλης, Μιχαήλ Ι.
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Thesis advisor
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Χριστοφίδης, Βασίλειος
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Reviewer
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Τσαμαρδινός, Ιωάννης
Τσακαλίδης, Παναγιώτης
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| Abstract |
The experimental evaluation of unsupervised anomaly detection algorithms is a constant
challenge within diverse research areas and applications domains. However, little is known
regarding the strengths and weaknesses of online anomaly detection methods and the impact of
their parameters. This paper elaborates on the design and development of a benchmark
framework to perform an extensive experiment study on tree and nearest-neighbor based
methods of top-notch unsupervised online outlier detectors (including their offline) in streaming
manner, across a wide variety of multivariate datasets contaminated by sub and full space
outliers. Initially, we present the semantics and functionalities of the detectors through a
comprehensive example. Then, we introduce the benchmark environment providing a descriptive
(meta) analysis of the datasets and implementation choices of detectors, posing also the set of
their hyper-parameters and candidate values. The fair evaluation of detectors is guaranteed
through an adequately analysis of critical methodological questions such as stream simulation
and partitioning, evaluation protocols and metrics, detectors optimization and ranking. Through
this study, we ascertain that online detectors not only approximate very well offline detectors
(2.296 vs 2.266, respectively; Ranking value) but also outperform them under certain conditions.
In addition, we surprisingly establish the robustness of online detectors' dynamic model on scaling
data and subspace dimensionality. Nevertheless, they shown a decreasing effectiveness while
scaling window parameters. We also examine the fundamentals of a dynamic model highlighting
the need for a forgetting mechanism. To the best of our knowledge, this is the most complete
online anomaly detection benchmark attempt on multivariate data.
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| Language |
English |
| Issue date |
2020-03-27 |
| Collection
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School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses
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Type of Work--Post-graduate theses
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| Permanent Link |
https://elocus.lib.uoc.gr//dlib/7/e/0/metadata-dlib-1585222534-496004-27760.tkl
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| Views |
1709 |
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