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Full Description
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.
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
