Analyzing vast amounts of enterprise cyber security data to find threats can be cumbersome. Cyber threat detection is also a continuous task, and because of financial pressure, companies have to find optimized solutions for this volume of data. We'll discuss the evolution of big data architectures used for cyber defense and how GPUs are allowing enterprises to efficiently improve threat detection. We'll discuss (1) briefly the evolution of traditional platforms to lambda architectures and ultimately GPU-accelerated solutions; (2) current GPU-accelerated database, analysis tools, and visualization technologies (such as MapD, BlazingDB, H2O.ai, Anaconda and Graphistry), and discuss the problems they solve; (3) the need to move beyond traditional rule based indicators of compromise and use a combination of machine learning, graph analytics, and deep learning to improve threat detection; and finally (4) our future plans to continue to advance GPU accelerated cyber security R&D as well as the GPU Open Analytics Initiative.
Hall: Hall F
Track: Cyber Security