Introduction
Background
Related work
A taxonomy and empirical analysis of clustering algorithms for traffic classification
Toward an efficient and accurate unsupervised feature selection
Optimizing feature selection to improve transport layer statistics quality
Optimality and stability of feature set for traffic classification
A privacy-perserving framework for traffic data publishing
A semi-supervised approach for network traffic labeling
A hybrid clustering-classification for accurate and efficient network classification
Conclusion.