Efficient Mining of the Multidimensional Traffic Cluster Hierarchy for Digesting, Visualization, and Anomaly Identification

@article{Wang2006EfficientMO,
  title={Efficient Mining of the Multidimensional Traffic Cluster Hierarchy for Digesting, Visualization, and Anomaly Identification},
  author={Jisheng Wang and David J. Miller and George Kesidis},
  journal={IEEE Journal on Selected Areas in Communications},
  year={2006},
  volume={24},
  pages={1929-1941}
}
Mining traffic to identify the dominant flows sent over a given link, over a specified time interval, is a valuable capability with applications to traffic auditing, simulation, visualization, as well as anomaly detection. Recently, Estan advanced a comprehensive data mining structure tailored for networking data-a parsimonious, multidimensional flow hierarchy, along with an algorithm for its construction. While they primarily targeted offline auditing, use in interactive traffic visualization… CONTINUE READING