Network Classification for Traffic Management - Anomaly Detection, Feature Selection, Clustering and Classification

This authored book investigates network traffic classification solutions by proposing transport-layer methods to achieve better run and operated enterprise-scale networks.

List of contents

• Chapter 1: Introduction


• Chapter 2: Background


• Chapter 3: Related work


• Chapter 4: A taxonomy and empirical analysis of clustering algorithms for traffic classification


• Chapter 5: Toward an efficient and accurate unsupervised feature selection


• Chapter 6: Optimizing feature selection to improve transport layer statistics quality


• Chapter 7: Optimality and stability of feature set for traffic classification


• Chapter 8: A privacy-preserving framework for traffic data publishing


• Chapter 9: A semi-supervised approach for network traffic labeling


• Chapter 10: A hybrid clustering-classification for accurate and efficient network classification


• Chapter 11: Conclusion

About the author

Zahir Tari is a full professor and discipline head of the School of Computer Science, RMIT University, Australia. His expertise is in the areas of system performance (e.g., cloud, IoT) as well as system security (e.g., SCADA, cloud).

Product details

• Authors: Abdulmohsen Almalawi, Abdulmohsen (Assistant Professor Almalawi, Adil Fahad, Adil (Assistant Professor Fahad, Zahir Tari, Zahir (Full Professor Tari, Xun Yi, Xun (Professor Yi
• Publisher: Institution of Engineering & Technology
• Languages: English
• Product format: Hardback
• Released: 31.03.2020
• EAN: 9781785619212
• ISBN: 978-1-78561-921-2
• No. of pages: 288
• Dimensions: 163 mm x 234 mm x 23 mm
• Weight: 590 g
• Series: Computing and Networks
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > Data communication, networks; COMPUTERS / Networking / General, Computers - Communications / Networking