29 citations · 32 across the 3 of their papers we have counts for
5 papers
Trajectory Planning with Deep Reinforcement Learning in High-Level Action Spaces
Kyle R. Williams, Rachel Schlossman, Daniel Whitten +8
This paper presents a technique for trajectory planning based on continuously parameterized high-level actions (motion primitives) of variable duration. This technique leverages de…
Self-Updating Models with Error Remediation
Justin E. Doak, Michael R. Smith, Joey B. Ingram
Many environments currently employ machine learning models for data processing and analytics that were built using a limited number of training data points. Once deployed, the mode…
Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Information Security
Michael R. Smith, Nicholas T. Johnson, Joe B. Ingram +6
Despite the potential of Machine learning (ML) to learn the behavior of malware, detect novel malware samples, and significantly improve information security (InfoSec) we see few,…
Tracking Cyber Adversaries with Adaptive Indicators of Compromise
Justin E. Doak, Joe B. Ingram, Sam A. Mulder +6
A forensics investigation after a breach often uncovers network and host indicators of compromise (IOCs) that can be deployed to sensors to allow early detection of the adversary i…
Dynamic Analysis of Executables to Detect and Characterize Malware
Michael R. Smith, Joe B. Ingram, Christopher C. Lamb +4
It is needed to ensure the integrity of systems that process sensitive information and control many aspects of everyday life. We examine the use of machine learning algorithms to d…