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Auflistung nach Schlagwort "Anomaly Detection"

1 - 4 von 4
  • Konferenzbeitrag
    AI Defenders: Machine learning driven anomaly detection in critical infrastructures
    (INFORMATIK 2024, 2024) Nebebe, Betelhem; Kröckel, Pavlina; Yatagha, Romarick; Edeh, Natasha; Waedt, Karl
    Previous studies have evaluated the suitability of different machine learning (ML) models for anomaly detection in critical infrastructures, which are pivotal due to the potential consequences of disruptions that can lead to safety risks, operational downtime, and financial losses. Ensuring robust anomaly detection for these systems within a company is vital to mitigate risks and maintain continuous operation. In this paper, we utilize a time-series labeled dataset obtained from a hydraulic model simulator (ELVEES simulator) to conduct a comprehensive and comparative analysis of various ML models. The study aims to demonstrate how different models effectively identify and respond to anomalies, underscoring the potential artificial intelligence (AI) driven systems to mitigate attacks. With the chosen approach, we expect to achieve the best performance in detecting two types of anomalies: point anomaly and contextual anomaly.
  • Textdokument
    Anomaly Detection in Motion Timeseries using the Bosch XDK and Dynamic Time Warping
    (SKILL 2021, 2021) Mejía, Julián Rico; Isaías, Oscar Aguilar Aguila; Paschapur, Priyanka
    This paper presents the development of an anomaly detector for robotic movements using the dynamic time warping (DTW) algorithm and its implementation in Matlab. Data was collected by mounting the Bosch Cross-Domain Development Kit (XDK) sensor on a collaborative robot arm (Cobot), aiming at industrial applications in need for motion anomaly detection during repetitive tasks. The paper discusses practical issues like parameter tuning as well as algorithmic variants such as de­coupling accelerometer and gyroscope data.
  • Konferenzbeitrag
    Anomaly Detection in Supermarket Refrigeration Systems using Transformer Models: A Comparative Study
    (INFORMATIK 2024, 2024) Meyer, Melina; Gergeleit, Martin; Krechel, Dirk
    This study investigates anomaly detection methods in supermarket refrigeration systems. A transformer-based model is introduced in this field and compared with LSTM autoencoders. The models are trained and evaluated using preprocessed refrigeration data, with parameters optimized for accuracy, recall, precision, and F1 score. Our findings aim to enhance system monitoring and maintenance strategies, ultimately improving reliability, energy efficiency, and operational excellence in supermarket refrigeration technology.
  • Zeitschriftenartikel
    Workshop “Dependability and Fault Tolerance”
    (FERS-Mitteilungen: Vol. 28, No. 1, 2010) Großpietsch, K.-E.; Herkersdorf, A.