Anomaly Classification in Industrial Internet of Things
ABSTRACT : This thesis presents an IIoT Anomaly Classification Framework designed to detect and categorize anomalies, including failures, attacks, and other significant events. The research addresses the critical need for robust anomaly detection and classification in IIoT systems by providing a com...
- Autores:
-
Rodríguez López, Martha Lucía
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2025
- Institución:
- Universidad de Antioquia
- Repositorio:
- Repositorio UdeA
- Idioma:
- eng
- OAI Identifier:
- oai:bibliotecadigital.udea.edu.co:10495/45192
- Acceso en línea:
- https://hdl.handle.net/10495/45192
- Palabra clave:
- Anomaly detection (Computer security)
Detección de anomalías (Seguridad informática)
Seguridad en computadores
Computer security
Confiabilidad (ingeniería)
Reliability (engineering)
Internet de las cosas
Internet of things
Industrial Internet of Things (IIoT)
http://aims.fao.org/aos/agrovoc/c_e4315b22
http://id.loc.gov/authorities/subjects/sh2005007675
- Rights
- openAccess
- License
- https://creativecommons.org/licenses/by-nc-sa/4.0/
| Summary: | ABSTRACT : This thesis presents an IIoT Anomaly Classification Framework designed to detect and categorize anomalies, including failures, attacks, and other significant events. The research addresses the critical need for robust anomaly detection and classification in IIoT systems by providing a comprehensive and scalable solution adaptable to various industrial contexts. The framework enhances modern industrial operations’ reliability, security, and efficiency, paving the way for more resilient and intelligent IIoT systems. |
|---|