I-Corps: SDNatics: Big Data Analytics of Software Defined Networks to Understand, Predict and Protect Critical Computer Networks

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Conventional computer network management has serious limitations on granularity, responsiveness and predictability, which can lead to network failures costing millions of dollars in critical domains, e.g., public safety and health care. Today's networks are currently managed under various enterprise policies and with state-of-the-art tools such as software defined networking (SDN) controllers. However, the faults and problems in the networks still arise because there are always cases to which predefined rules and algorithms do not respond well, causing traffic flooding, congestions, or vulnerability to attacks. Understanding in-depth of the network behavior patterns and applying the knowledge in network management and planning are fundamental to providing new solutions and best practices.This team has developed a suite of protocols and analytics methods to understand network dynamics by using software-defined networking (SDN) and machine learning technologies. With this information the team is able to report abnormal network behavior and alert potential faults. This allows for much more of the network to be managed algorithmically, automatically, and intelligently. Specifically, this team's technology is able to track network flows to a much higher granularity (i.e. resolution) using SDN technology. However, while current SDN approaches are better than traditional networks in defining and enforcing rules around network flows, they do not have a capability to analyze in-depth usage behavior and dynamically apply such information to improve network management and planning. By using machine learning techniques (i.e. Deep Learning), the team will develop the SDNatics software platform that will allow for more advance uses of SDN. Such improvements should improve security, predictability, speed, and data volume in network systems.