Self-learning Anomaly Detection in Industrial Production
| dc.contributor.author | Meshram, Ankush | |
| dc.date.accessioned | 2023-06-26T14:36:24Z | |
| dc.date.available | 2023-06-26T14:36:24Z | |
| dc.date.issued | 2023 | |
| dc.identifier.uri | https://library.oapen.org/handle/20.500.12657/63682 | |
| dc.description.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. | en_US |
| dc.language | English | en_US |
| dc.relation.ispartofseries | Karlsruher Schriften zur Anthropomatik | en_US |
| dc.subject.classification | thema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientists | en_US |
| dc.subject.other | Industrielles Steuerungssystem; Netzwerksicherheit; Netzwerk-Intrusion-Detection-System; Anomalieerkennung; selbstlernend; Industrial Control System; Network Security; Network Intrusion Detection System; Anomaly Detection; self-learning | en_US |
| dc.title | Self-learning Anomaly Detection in Industrial Production | en_US |
| dc.type | book | |
| oapen.identifier.doi | 10.5445/KSP/1000152715 | en_US |
| oapen.relation.isPublishedBy | 44e29711-8d53-496b-85cc-3d10c9469be9 | en_US |
| oapen.series.number | 59 | en_US |
| oapen.pages | 224 | en_US |
