|Date:||7 January-10 January 2013|
|Location:||Hyatt Regency Albuquerque, Albuquerque, NM|
|Organizer:||Software Engineering Institute at Carnegie Mellon|
Flow is an abstraction of network traffic in which packets are aggregated by common attributes over time. This year's conference will focus on the challenges of "Analysis at Scale." In large network environments, flow data helps to provide a scalable way of seeing the big picture, as well as a streamlined platform for highlighting patterns of malicious behavior over time.
More and more commercial tools and platforms are available for collecting and storing not only flow data, but large volumes of other data such as DNS information, packet capture, security logs, and incident reports. How do we refine this "Big Data" into knowledge? How do we design methods for aggregated analyses at the network edge? How do we build systems for monitoring thousands or millions of assets at once?
The era of Big Data has brought with it the need to integrate cross-disciplinary expertise—in numerical methods, system design, software engineering, visualization, and analytical thinking—with the goal of gaining awareness and insight from raw records. Analysis of Big Data at the ISP and carrier-class network level adds challenges of data abstraction, context, and scope that must be addressed with the implementation of any system designed to help operational analysts use this data to learn about network threats.
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