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.

• 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