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    • Self-learning Anomaly Detection in Industrial Production

    Ankush Meshram

    Band 59 von Karlsruher Schriften zur Anthropomatik
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    Configuring an anomaly-based Network Intrusion Detection System for cybersecurity of an industrial system in the absence of information on networking infrastructure and programmed deterministic industrial process is challenging. Within the research work, different self-learning frameworks to analyze passively captured network traces from PROFINET-based industrial system for protocol-based and process behavior-based anomaly detection are developed, and evaluated on a real-world industrial system.

    Umfang: XIV, 193 S.

    Preis: 44.00 €

    Fächer:

    Informatik und Mathematik 

    Schlagwörter:

    Industrielles Steuerungssystem Netzwerksicherheit Netzwerk-Intrusion-Detection-System Anomalieerkennung selbstlernend Industrial Control System Network Security Network Intrusion Detection System Anomaly Detection self-learning

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    Empfohlene Zitierweise
    Meshram, A. 2023. Self-learning Anomaly Detection in Industrial Production. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000152715
    Meshram, A., 2023. Self-learning Anomaly Detection in Industrial Production. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000152715
    Meshram, A. Self-learning Anomaly Detection in Industrial Production. KIT Scientific Publishing, 2023. DOI: https://doi.org/10.5445/KSP/1000152715
    Meshram, A. (2023). Self-learning Anomaly Detection in Industrial Production. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000152715
    Meshram, Ankush. 2023. Self-learning Anomaly Detection in Industrial Production. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000152715



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    Weitere Informationen

    Veröffentlicht am 19. Juni 2023

    Sprache

    Englisch

    Seitenanzahl:

    226

    ISBN
    Paperback 978-3-7315-1257-8

    DOI
    https://doi.org/10.5445/KSP/1000152715