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Self-learning Anomaly Detection in Industrial Production

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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...

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Bibliographic Details
Main Author: Meshram, Ankush (auth)
Format: Electronic Book Chapter
Language:English
Published: KIT Scientific Publishing 2023
Series:Karlsruher Schriften zur Anthropomatik
Subjects:
Maths for computer scientists
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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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.
Physical Description:1 electronic resource (224 p.)
ISBN:KSP/1000152715
Access:Open Access

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