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Network Classification for Traffic Management: Anomaly detection, feature selection, clustering and classification

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Publication Year: 2020

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.

Inspec keywords: computer network security; learning (artificial intelligence); data privacy; pattern clustering; feature selection; telecommunication traffic; computer network management; pattern classification

Other keywords: traffic data publishing; privacy preservation; unsupervised feature selection; clustering algorithms; hybrid clustering-classification; semisupervised network traffic labelling; feature selection; anomaly detection; transport layer statistics quality; enterprise-scale networks; network traffic classification; traffic management

Subjects: Knowledge engineering techniques; Network management; General and management topics; General electrical engineering topics; Computer communications; Computer installation management; Computing security management; Computer networks and techniques; Data handling techniques

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