Anomaly Detection in Cybersecurity: Unsupervised, Graph-Based and Supervised Learning Methods in Adversarial Environments. - David A. Bierbrauer; Alexander Chang; Will Kritzer; Nathaniel D. Bastian (CoRR, 2021)

Payload-Byte: A Tool for Extracting and Labeling Packet Capture Files of Modern Network Intrusion Detection Datasets. - Yasir Ali Farrukh; Irfan Khan 001; Syed Wali; David A. Bierbrauer; John A. Pavlik; Nathaniel D. Bastian (BDCAT, 2022)

Uncertainty-Quantified, Robust Deep Learning for Network Intrusion Detection. - Joshua A. Wong; Alexander M. Berenbeim; David A. Bierbrauer; Nathaniel D. Bastian (WSC, 2023)

Transfer learning for raw network traffic detection. - David A. Bierbrauer; Michael J. De Lucia; Krishna Reddy; Paul Maxwell; Nathaniel D. Bastian (Expert Syst. Appl., 2023)

Offline Reinforcement Learning for Autonomous Cyber Defense Agents. - Alexander Wei; David A. Bierbrauer; Emily A. Nack; John A. Pavlik; Nathaniel D. Bastian (WSC, 2024)

ACI IoT Network Traffic Dataset 2023. - Nathaniel D. Bastian; David A. Bierbrauer; Morgan C. McKenzie; Emily A. Nack (IEEE DataPort, 2024)

Data-efficient Federated Learning for Edge Network Intrusion Detection. - David A. Bierbrauer; Sean M. Coffey; Mikal R. Willeke; John D. Beggs; Nathaniel D. Bastian (Eng. Appl. Artif. Intell., 2025)