Operators of large-scale enterprises and ISPs need to understand the type of traffic that their networks handle, and emergent applications and traffic behavior, in order to better service them and detect anomalies. Also, better assessment of the carried traffic will inform network planning and security. Particularly for private enterprise networks, monitoring methods can be used to detect inappropriate traffic classes indicating unprofessional (or at least unauthorized) activity. This work will adapt and innovate methods of unsupervised machine learning to classify traffic flows to ascertain the types of end-user applications which are active in an enterprise network.
The broader impact of the project will include explaining networking concepts to a wider audience of machine learning researchers, and vice versa so that the newly developed techniques will have wide dissemination to the networking community, as well as to other domains in science and engineering. Also, cross-disciplinary graduate-level courseware on applications of machine learning to network flow data and related concepts will be developed and disseminated. More practical developments will be achieved through collaboration with industrial partners. Finally, the project will aim to support graduate students from under-represented groups in computer science and engineering, particularly women.
The primary technical merit of the research to be conducted will pertain to the high-volume and considerably complex network data under consideration, including prevalent short flows, given limited computing and communication resources to do so.
|Effective start/end date||9/1/09 → 8/31/13|
- National Science Foundation: $299,301.00