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Title	Network Classification For Traffic Management: Anomaly Detection, Feature Selection, Clustering And Classification
ID_Doc	38981
Authors	Tari Z.; Fahad A.; Almalawi A.; Yi X.
Year	2020
Published	Network Classification for Traffic Management
DOI	http://dx.doi.org/10.1049/PBPC032E
Abstract	With the massive increase of data and traffic on the Internet within the 5G, IoT and smart cities frameworks, current network classification and analysis techniques are falling short. Novel approaches using machine learning algorithms are needed to cope with and manage real-world network traffic, including supervised, semi-supervised, and unsupervised classification techniques. Accurate and effective classification of network traffic will lead to better quality of service and more secure and manageable networks. This authored book investigates network traffic classification solutions by proposing transport layer methods to achieve better run and operated enterprise-scale networks. The authors explore novel methods for enhancing network statistics at the transport layer, helping to identify optimal feature selection through a global optimization approach and providing automatic labelling for raw traffic through a SemTra framework to maintain provable privacy on information disclosure properties. © The Institution of Engineering and Technology 2020.
Author Keywords	Anomaly detection; Clustering algorithms; Enterprise-scale networks; Feature selection; Hybrid clustering-classification; Network traffic classification; Privacy preservation; Semisupervised network traffic labelling; Traffic data publishing; Traffic management; Transport layer statistics quality; Unsupervised feature selection

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