Development contract awarded to CA for simplifying identification methods for determining network security threats
CA Labs has received an $815,000 research award from the US Department of Homeland Security’s (DHS) Science and Technology (S&T) Directorate to drive innovation in network traffic flow analysis. Together with research partner Dalhousie University of Halifax, Canada, CA Labs will explore new methods for visualizing massive quantities of network data, enabling the extraction of vital information from network traffic patterns which can accelerate the identification of potential security threats. CA was one of thirteen organizations, out of 700 registrants, to receive an award under S&T Directorate’s recent Broad Agency Announcement (BAA 07-09). Upon completion of the research, CA Labs expects to deliver an innovative visualization framework that will be deployed within DHS and also released to the open source community. “CA is committed to advancing technology research in areas of strategic importance to our company and to our nation,” said Gabby Silberman, senior vice president of CA Labs. “Through CA Labs, we partner with both internal and external organizations to further innovation in enterprise IT management. We engage with academia, professional associations, industry standards bodies, customers and partners to explore novel products and emerging technologies. We are pleased to receive this important award from the US Department of Homeland Security.” Under the award program, the US government fosters the creation of new security technology for the protection of the US cyber infrastructure. For CA Labs, the award will fund the delivery of a visualization framework that can easily present vast amounts of network information to facilitate security analysis across large, distributed networks. CA Labs, established in 2005 to strengthen relationships between research communities and CA, conducts advanced research in areas related to enterprise IT management. Examples of current research projects include: data mining for role-based access control; autonomic web service management; and knowledge management processes and modelling.
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