Self-learning Anomaly Detection in Industrial Production
Overview & Synopsis
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.
Indexed from repository: OAPEN Library
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www.oapen.org)
Document Information
| Document Title | Self-learning Anomaly Detection in Industrial Production |
|---|---|
| Author / Organization | Meshram, Ankush |
| Subject & Discipline | Industrielles Steuerungssystem; Netzwerksicherheit; Netzwerk-Intrusion-Detection-System; Anomalieerkennung; selbstlernend; Industrial Control System; Network Security; Network Intrusion Detection System; Anomaly Detection; self-learning |
| Categories | Humanities & Social Sciences |
| Publication Year | 2023 |
| License / Rights | Open Access / Creative Commons |
| Resource Verification | Listed PDF (Verification Pending) |
| Original Host Website |
OAPEN Library
(www.oapen.org)
|
| File Format | application/pdf (Portable Document Format) |
| Language | EN |
| Catalog ID | DOC-299211 |
| Original Source Link | Visit Source Page on www.oapen.org » |