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Integrating Dynamic Thresholding in Anomaly Detection on Water Treatment Facilities

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dc.contributor.authorYatagha, Romarick
dc.contributor.authorOeztuerk, Esin
dc.contributor.authorNebebe, Betelhem
dc.contributor.authorEdeh, Natasha
dc.contributor.authorMejri, Oumayma
dc.contributor.editorKlein, Maike
dc.contributor.editorKrupka, Daniel
dc.contributor.editorWinter, Cornelia
dc.contributor.editorGergeleit, Martin
dc.contributor.editorMartin, Ludger
dc.date.accessioned2024-10-21T18:24:17Z
dc.date.available2024-10-21T18:24:17Z
dc.date.issued2024
dc.description.abstractWith the growing complexity of cyber-physical systems (CPS), adaptive and robust monitoring solutions are increasingly crucial for ensuring operational reliability and safety. Anomaly detection is a critical component of monitoring systems, particularly in dynamic environments such as water management systems, where operational regimes can vary significantly over time. Traditional static thresholding techniques, which use a single fixed threshold for the entire monitoring process, are often inadequate due to their inability to adapt to changing data patterns, leading to high rates of false positives and missed detections. This paper explores the limitations of static thresholding and presents a comparative analysis with more adaptive approaches. We first discuss the use of static thresholds for each regime shift, which provides some improvement but still falls short in accommodating gradual or unexpected changes. Subsequently, we introduce a dynamic thresholding method based on the Autoregressive Integrated Moving Average (ARIMA) model. This approach continuously adjusts thresholds in real time, effectively accounting for evolving data patterns and regime shifts. Our evaluation, conducted on synthetic water level data with known anomalies, demonstrates that dynamic thresholding significantly outperforms static methods. Specifically, dynamic thresholding achieves an accuracy of 99%, precision of 78%, recall of 88%, and an F1-score of 82%, highlighting its robustness and adaptability. These results underscore the potential of dynamic thresholding techniques to enhance anomaly detection in complex, variable environments.en
dc.identifier.doi10.18420/inf2024_168
dc.identifier.isbn978-3-88579-746-3
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/45145
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofINFORMATIK 2024
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-352
dc.subjectContinual Learning
dc.subjectMonitoring System
dc.subjectCyber Physical System
dc.titleIntegrating Dynamic Thresholding in Anomaly Detection on Water Treatment Facilitiesen
dc.typeText/Conference Paper
gi.citation.endPage1945
gi.citation.publisherPlaceBonn
gi.citation.startPage1939
gi.conference.date24.-26. September 2024
gi.conference.locationWiesbaden
gi.conference.sessiontitle9th IACS WS'24

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