Traditional approaches to cybersecurity are falling short. The current cost-to-value ratio of big data cyberdefense platforms forces organizations to only monitor a fraction of their network data, which leaves them vulnerable to attacks.
Joshua Patterson and Mike Wendt explain how NVIDIA used GPU-accelerated open source technologies to improve its cyberdefense platforms by leveraging software from the GPU Open Analytics Initiative (GOAI) and how the company accelerated anomaly detection with more efficient machine learning models, faster deployment, and more granular data exploration. Josh and Mike discuss how NVIDIA parses and forwards data to its production SIEM environment, GPU cluster, and R&D environment. They then share methods for adding new GPU-accelerated applications to the production SIEM environment based on the efficacy of R&D and explore the performance improvements of using GPUs for scale-out data warehousing, real-time dashboarding, and threat hunting applications. Josh and Mike conclude by outlining how NVIDIA is using GPUs to improve anomaly detection with more efficient machine learning, deep learning, and inferencing techniques.
Joshua Patterson is the director of applied solutions engineering at NVIDIA. Previously, Josh worked with leading experts across the public and private sectors and academia to build a next-generation cyberdefense platform. He was also a White House Presidential Innovation Fellow. His current passions are graph analytics, machine learning, and GPU data acceleration. Josh also loves storytelling with data and creating interactive data visualizations. He holds a BA in economics from the University of North Carolina at Chapel Hill and an MA in economics from the University of South Carolina’s Moore School of Business.
Mike Wendt is a manager of applied solutions engineering at NVIDIA. His research work focuses on leveraging GPUs for big data analytics, data visualizations, and stream processing. In addition, Mike has focused on developing new ways of visualizing data and the scalable architectures to support them. Previously, Mike led engineering work on big data technologies like Hadoop, Datastax Cassandra, Storm, Spark, and others. He holds a BS in computer engineering from the University of Maryland.
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