Self-learning Anomaly Detection in Industrial Production
Abstract
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.
Keywords
Industrielles Steuerungssystem; Netzwerksicherheit; Netzwerk-Intrusion-Detection-System; Anomalieerkennung; selbstlernend; Industrial Control System; Network Security; Network Intrusion Detection System; Anomaly Detection; self-learningDOI
10.5445/KSP/1000152715ISBN
9783731512578Publisher
KIT Scientific PublishingPublisher website
https://www.ksp.kit.edu/index.php?link=shop&sort=allPublication date and place
2023Series
Karlsruher Schriften zur Anthropomatik, 59Classification
Maths for computer scientists