Profiling internet backbone traffic

Recent spates of cyber-attacks and frequent emergence of applications affecting Internet traffic dynamics have made it imperative to develop effective techniques that can extract, and make sense of, significant communication patterns from Internet traffic data for use in network operations and security management. In this paper, we present a general methodology for building comprehensive behavior profiles of Internet backbone traffic in terms of communication patterns of end-hosts and services. Relying on data mining and information-theoretic techniques, the methodology consists of significant cluster extraction, automatic behavior classification and structural modeling for in-depth interpretive analyses. We validate the methodology using data sets from the core of the Internet. The results demonstrate that it indeed can identify common traffic profiles as well as anomalous behavior patterns that are of interest to network operators and security analysts.