Integrating Dynamic Thresholding in Anomaly Detection on Water Treatment Facilities
| dc.contributor.author | Yatagha, Romarick | |
| dc.contributor.author | Oeztuerk, Esin | |
| dc.contributor.author | Nebebe, Betelhem | |
| dc.contributor.author | Edeh, Natasha | |
| dc.contributor.author | Mejri, Oumayma | |
| dc.contributor.editor | Klein, Maike | |
| dc.contributor.editor | Krupka, Daniel | |
| dc.contributor.editor | Winter, Cornelia | |
| dc.contributor.editor | Gergeleit, Martin | |
| dc.contributor.editor | Martin, Ludger | |
| dc.date.accessioned | 2024-10-21T18:24:17Z | |
| dc.date.available | 2024-10-21T18:24:17Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | With 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.doi | 10.18420/inf2024_168 | |
| dc.identifier.eissn | 2944-7682 | |
| dc.identifier.isbn | 978-3-88579-746-3 | |
| dc.identifier.issn | 2944-7682 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/45145 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | INFORMATIK 2024 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-352 | |
| dc.subject | Continual Learning | |
| dc.subject | Monitoring System | |
| dc.subject | Cyber Physical System | |
| dc.title | Integrating Dynamic Thresholding in Anomaly Detection on Water Treatment Facilities | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 1945 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 1939 | |
| gi.conference.date | 24.-26. September 2024 | |
| gi.conference.location | Wiesbaden | |
| gi.conference.sessiontitle | 9th IACS WS'24 |
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