Network classification for traffic management : anomaly detection, feature selection, clustering and classification / Zahir Tari, Adil Fahad, Abdulmohsen Almalawi and Xun Yi
Published
Stevenage : Institution of Engineering and Technology, 2020
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
Summary
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; transport layer statistics quality; feature set; privacy preserving framework for traffic data publishing; semi-supervised approach for network traffic labelling; and hybrid clustering-classification
Bibliography
Includes bibliographical references and index (pages 261-268)
Notes
Online resource; title from digital title page (viewed on June 01, 2020)