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dc.contributor.authorMeshram, Ankush
dc.date.accessioned2023-06-26T14:36:24Z
dc.date.available2023-06-26T14:36:24Z
dc.date.issued2023
dc.identifier.urihttps://library.oapen.org/handle/20.500.12657/63682
dc.description.abstractConfiguring 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.en_US
dc.languageEnglishen_US
dc.relation.ispartofseriesKarlsruher Schriften zur Anthropomatiken_US
dc.subject.classificationthema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientistsen_US
dc.subject.otherIndustrielles Steuerungssystem; Netzwerksicherheit; Netzwerk-Intrusion-Detection-System; Anomalieerkennung; selbstlernend; Industrial Control System; Network Security; Network Intrusion Detection System; Anomaly Detection; self-learningen_US
dc.titleSelf-learning Anomaly Detection in Industrial Productionen_US
dc.typebook
oapen.identifier.doi10.5445/KSP/1000152715en_US
oapen.relation.isPublishedBy44e29711-8d53-496b-85cc-3d10c9469be9en_US
oapen.series.number59en_US
oapen.pages224en_US
peerreview.anonymityAll identities known
peerreview.id51a542ec-eaeb-47c2-861d-6022e981a97a
peerreview.open.reviewNo
peerreview.publish.responsibilityBooks or series editor
peerreview.review.stagePre-publication
peerreview.review.typeFull text
peerreview.reviewer.typeEditorial board member
peerreview.reviewer.typeExternal peer reviewer
peerreview.titleDissertations in Series (Dissertationen in Schriftenreihe)


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