Network classification for traffic management : anomaly detection, feature selection, clustering and classification /
The book investigates network traffic classification solutions by proposing transport-layer methods to achieve better run and operated enterprise-scale networks. It deals with the following subjects: traffic management; anomaly detection; clustering algorithms; unsupervised feature selection; transp...
Clasificación: | Libro Electrónico |
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Autores principales: | , , , |
Formato: | Electrónico eBook |
Idioma: | Inglés |
Publicado: |
Stevenage :
Institution of Engineering and Technology,
2020.
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Colección: | IET computing series ;
32. |
Temas: | |
Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Introduction
- Background
- Related work
- A taxonomy and empirical analysis of clustering algorithms for traffic classification
- Toward an efficient and accurate unsupervised feature selection
- Optimizing feature selection to improve transport layer statistics quality
- Optimality and stability of feature set for traffic classification
- A privacy-perserving framework for traffic data publishing
- A semi-supervised approach for network traffic labeling
- A hybrid clustering-classification for accurate and efficient network classification
- Conclusion.