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Improving Anomaly Detection with Adaptive Dynamic Threshold: A Review and Enhanced Method

Attar, Arman Aghaei 1; Fabarisov, Tagir 2; Bao, Kaibin ORCID iD icon 1; Hagenmeyer, Veit ORCID iD icon 1
1 Institut für Automation und angewandte Informatik (IAI), Karlsruher Institut für Technologie (KIT)
2 Universität Stuttgart (Uni Stuttgart)

Abstract:

In fast-growing modern cyber-physical systems, reliability plays a vital role. Effective anomaly detection, which identifies security and safety issues at early stage, is essential for ensuring system reliability. While much research has focused on anomaly detection techniques, fewer studies address a key challenge, setting precise and responsive thresholds for anomaly detection.
In this study, we conduct a comprehensive review of current threshold setting methods. Thereafter, we introduce a novel approach for adaptive threshold setting. Our method is tailored for diverse safety and security tasks and is tested on a safety-critical exoskeleton model and a cybersecurity scenario for energy systems. The results demonstrate that our approach could enhance the threshold setting for anomaly detection in CPS.

Zugehörige Institution(en) am KIT Institut für Automation und angewandte Informatik (IAI)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2024
Sprache Englisch
Identifikator ISBN: 979-8-3503-5449-2
KITopen-ID: 1000178584
HGF-Programm 37.12.01 (POF IV, LK 01) Digitalization & System Technology for Flexibility Solutions
Weitere HGF-Programme 46.23.02 (POF IV, LK 01) Engineering Security for Energy Systems
Erschienen in 2024 8th International Conference on System Reliability and Safety (ICSRS)
Veranstaltung 8th International Conference on System Reliability and Safety (ICSRS 2024), Sicily, Italy, 20.11.2024 – 22.11.2024
Seiten 5 S.
Schlagwörter Anomaly Detection, Threshold Setting, Cyber-Physical Systems, Time Series

Postprint §
DOI: 10.5445/IR/1000178584
Veröffentlicht am 31.01.2025
Seitenaufrufe: 47
seit 31.01.2025
Downloads: 62
seit 01.02.2025
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