Jon Barker researches and develops deep learning applications at NVIDIA. Most recently Jon has researched the use of deep learning for document classification, malware classification and audio enhancement. Previously Jon supported NVIDIA's customers developing deep learning applications. Prior to joining NVIDIA Jon spent nine years as a research scientist in the UK and US defense communities, predominantly focusing on remote sensing. Jon has a PhD in Mathematics from the University of Southampton, UK.
Rob Brandon is currently a security researcher with the Booz-Allen Hamilton Dark labs. He has over a decade of experience in the security field, primarily in the areas of network traffic analysis, forensics, and reverse engineering. He completed a PhD in computer science with the University of Maryland, Baltimore County on the topic of representing executable code using deep neural networks. His primary research interests include novel ways to represent cybersecurity data and machine augmentation of human cognition.
Edward Raff is a computer scientist at Booz Allen Hamilton, specializing in machine learning problems and solutions. As the author of the JSAT library, Edward has extensive experience implementing all manner of algorithms. In particular, he has worked on problems involving bioinformatics, signal classification, sentiment analysis, real-time object tracking, and change detection. He currently works at the Laboratory for Physical Sciences researching new methods of applying deep learning to cybersecurity, and in particular malware classification and analysis. Edward holds a bachelor's and master's degree from Purdue University, and is working on a Ph.D. at the University of Maryland, Baltimore County.
Jared Sylvester joined Booz Allen Hamilton in 2014 as a member of the Strategic Innovation Group, where he has been doing machine learning research focusing on cybersecurity applications and neuromorphic computing. Prior to that he got his doctorate in AI at the University of Maryland, working in both the Computer Science Department doing neural network cognitive modeling, and the Marketing department doing social network analytics. He lives in Ellicott City, Maryland with his wife, two children, and terrier, and enjoys algorithmic art, calligraphy, bread baking and wood working.
Malware is a problem of increasing impact as our society becomes more dependent on an infrastructure of networked computers. Detecting malware is not a trivial task, and numerous companies and startups are researching ways that machine learning could help solve this problem. At the Laboratory for Physical Sciences, we've been researching new ways of applying deep learning to this problem. In contrast to most works in the subject area, ours has been done while minimizing the amount of domain knowledge used. We'll share our results in training a neural network that processes an entire binary from scratch, giving us a malware detection system that requires no knowledge to train. This gives our approach considerable flexibility and can be repurposed to new domains with minimal effort. However, it also poses a number of machine learning and engineering challenges to overcome.