Securing networks increasingly requires the ability to collect, process, and understand large, continuous streams of network data. Three of the biggest challenges in securing networks are: (1) mining streams of event logs and network traffic to extract actionable information about the state of the network; (2) adjusting policies and configurations in response to changing conditions and threats; (3) having some assurance that the resulting network configurations are correct, predictable, and secure.
Discover the role of machine learning in helping operators manage and secure today’s networks. We will describe past work in applying machine learning to problems in network anomaly detection systems, as well as the potential for machine learning methods to help network operators address a broader range of cybersecurity problems, ranging from insider threat detection to decisions about deployment of limited resources for security functions.
Learn about how machine learning over network data can be applied to:
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Alexander Gray is CTO at Skytree and Associate Professor in the College of Computing at Georgia Tech. His work has focused on algorithmic techniques for making machine learning tractable on massive datasets. He began working with large-scale scientific data in 1993 at NASA’s Jet Propulsion Laboratory in its Machine Learning Systems Group. He recently served on the National Academy of Sciences Committee on the Analysis of Massive Data, as a Kavli Scholar, and a Berkeley Simons Fellow, and is a frequent advisor and speaker on the topic of machine learning on big data in academia, science, and industry.
Nick Feamster is a professor in the College of Computing at Georgia Tech. He received his Ph.D. in Computer Science from MIT in 2005, and his S.B. and M.Eng. degrees in Electrical Engineering and Computer Science from MIT in 2000 and 2001, respectively. His research focuses on many aspects of computer networking and networked systems, with a focus on network operations, network security, and censorship resistant communication systems. In December 2008, he received the Presidential Early Career Award for Scientists and Engineers (PECASE) for his contributions to cybersecurity, notably spam filtering.