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Self-learning Anomaly Detection in Industrial Production

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...

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Hovedforfatter: Meshram, Ankush
Format: Online
Sprog:engelsk
Udgivet: KIT Scientific Publishing 2023
Fag:
Industrielles Steuerungssystem; Netzwerksicherheit; Netzwerk-Intrusion-Detection-System; Anomalieerkennung; selbstlernend; Industrial Control System; Network Security; Network Intrusion Detection System; Anomaly Detection; self-learning
thema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientists
Online adgang:https://library.oapen.org/handle/20.500.12657/63682
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Summary: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.

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