Automatically inferring patterns of resource consumption in network traffic
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
FaRNet: Fast recognition of high-dimensional patterns from big network traffic data
Computer Networks: The International Journal of Computer and Telecommunications Networking
| Hi-index | 0.00 |
Extracting knowledge from big network traffic data is a matter of foremost importance for multiple purposes ranging from trend analysis or network troubleshooting to capacity planning or traffic classification. An extremely useful approach to profile traffic is to extract and display to a network administrator the multi-dimensional hierarchical heavy hitters (HHHs) of a dataset. However, existing schemes for computing HHHs have several limitations: 1) they require significant computational overhead; 2) they do not scale to high dimensional data; and 3) they are not easily extensible. In this paper, we introduce a fundamentally new approach for extracting HHHs based on generalized frequent item-set mining (FIM), which allows to process traffic data much more efficiently and scales to much higher dimensional data than present schemes. Based on generalized FIM, we build and evaluate a traffic profiling system we call FaRNet. Our comparison with AutoFocus, which is the most related tool of similar nature, shows that FaRNet is up to three orders of magnitude faster.