Abstract
The problem of anomaly detection is a critical topic across application domains and is the subject of extensive research. Applications include finding frauds and intrusions, warning on robot safety, and many others. Standard approaches in this field exploit simple or complex system models, created by experts using detailed domain knowledge.
In this paper, we put forth a statistics-based anomaly detector motivated by the fact that anomalies are sparse by their very nature. Powerful sparsity directed algorithms—namely Robust Principal Component Analysis and the Group Fused LASSO—form the basis of the methodology. Our novel unsupervised single-step solution imposes a convex optimisation task on the vector time series data of the monitored system by employing group-structured, switching and robust regularisation techniques.
We evaluated our method on data generated by using a Baxter robot arm that was disturbed randomly by a human operator. Our procedure was able to outperform two baseline schemes in terms of \(F_1\) score. Generalisations to more complex dynamical scenarios are desired.
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Acknowledgements
Supported by the European Union and co-financed by the European Social Fund (TÁMOP 4.2.1./B-09/1/KMR-2010-0003) and by the EIT Digital grant on CPS for Smart Factories.
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Milacski, Z.Á., Ludersdorfer, M., Lőrincz, A., van der Smagt, P. (2015). Robust Detection of Anomalies via Sparse Methods. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9491. Springer, Cham. https://doi.org/10.1007/978-3-319-26555-1_47
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DOI: https://doi.org/10.1007/978-3-319-26555-1_47
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