J. Ingram

Trajectory Planning With Deep Reinforcement Learning in High-Level Action Spaces

Kyle Williams, D. Whitten, A. Mazumdar, 2021, IEEE Transactions on Aerospace and Electronic Systems.

Self-Updating Models with Error Remediation pdf

Michael R. Smith, Justin E. Doak, Joey B. Ingram, 2020, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II.

Online anomaly detection for multi‐source VMware using a distributed streaming framework

Bhavani M. Thuraisingham, Latifur Khan, Sadi Evren Seker, 2016, Softw. Pract. Exp..

Spark-based anomaly detection over multi-source VMware performance data in real-time

Bhavani M. Thuraisingham, Latifur Khan, Mohiuddin Solaimani, 2014, 2014 IEEE Symposium on Computational Intelligence in Cyber Security (CICS).

Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Malware Analysis

Michael R. Smith, Nicholas T. Johnson, Joe B. Ingram, 2020, AISec@CCS.

Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Information Security pdf

Michael R. Smith, Joe B. Ingram, Armida J. Carbajal, 2020, ArXiv.

Dynamic Analysis of Executables to Detect and Characterize Malware

Conrad D. James, Joe B. Ingram, Christopher C. Lamb, 2017, 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA).

Host-Based Anomaly Detection Using Learning Techniques

Latifur Khan, Ken Chiang, Ahmad M. Mustafa, 2013, 2013 IEEE 13th International Conference on Data Mining Workshops.

Streaming Malware Classification in the Presence of Concept Drift and Class Imbalance

W. Philip Kegelmeyer, Ken Chiang, Joey Burton Ingram, 2013, 2013 12th International Conference on Machine Learning and Applications.

Tracking Cyber Adversaries with Adaptive Indicators of Compromise

Conrad D. James, Joe B. Ingram, James B. Aimone, 2017, 2017 International Conference on Computational Science and Computational Intelligence (CSCI).