Vai al contenuto principale della pagina
| Titolo: |
Self-learning Anomaly Detection in Industrial Production
|
| Pubblicazione: | KIT Scientific Publishing |
| Soggetto topico: | Computing and information technology |
| Computer science | |
| Mathematical theory of computation | |
| Industrial control systems | |
| Network security | |
| Network intrusion detection systems | |
| Altri autori: |
MeshramAnkush
|
| Sommario/riassunto: | 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. |
| Titolo autorizzato: | Self-learning Anomaly Detection in Industrial Production ![]() |
| ISBN: | 9783731512578 |
| 3731512572 | |
| Formato: | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione: | Inglese |
| Record Nr.: | 9910805897403321 |
| Lo trovi qui: | Univ. Federico II |
| Opac: | Controlla la disponibilità qui |