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

• Main Authors: Tari, Zahir, Fahad, Adil, Almalawi, Abdulmohsen, Yi, Xun
• Format: eBook
• Language: English
• Published: Stevenage The Institution of Engineering and Technology 2020; Institution of Engineering and Technology (The IET); Institution of Engineering & Technology; Institution of Engineering and Technology
• Edition: 1
• Series: Computing and Networks
• Subjects: Computer Hardware Engineering; Computer network protocols; COMPUTERS; General References; Network management; Networking; Networks & Sensors; Software Engineering; Telecommunication; pattern classification; computer network management; pattern clustering; data privacy; learning (artificial intelligence); computer network security; feature selection; telecommunication traffic
• ISBN: 1785619217; 9781785619212
• DOI: 10.1049/PBPC032E

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.