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

Network Classification for Traffic Management: Anomaly detection, feature selection, clustering and classification (Computing and Networks) Zahir Tari, Adil Fahad, Abdulmohsen Almalawi, Xun Yi
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. Categories:
Computers – Networking
Year:
2020
Publisher:
Institution of Engineering and Technology
Language:
english
Pages:
288 / 276
ISBN 10:
1785619217
ISBN 13:
9781785619212
File:
80 MB