Back in Black: A Comparative Evaluation of Recent State-Of-The-Art Black-Box Attacks. - Kaleel Mahmood; Rigel Mahmood; Ethan Rathbun; Marten van Dijk (IEEE Access, 2022)

Securing the Spike: On the Transferabilty and Security of Spiking Neural Networks to Adversarial Examples. - Nuo Xu; Kaleel Mahmood; Haowen Fang; Ethan Rathbun; Caiwen Ding; Wujie Wen (CoRR, 2022)

Game Theoretic Mixed Experts for Combinational Adversarial Machine Learning. - Ethan Rathbun; Kaleel Mahmood; Sohaib Ahmad; Caiwen Ding; Marten van Dijk (CoRR, 2022)

SleeperNets: Universal Backdoor Poisoning Attacks Against Reinforcement Learning Agents. - Ethan Rathbun; Christopher Amato; Alina Oprea (NeurIPS, 2024)

Distilling Adversarial Robustness Using Heterogeneous Teachers. - Jieren Deng; Aaron Palmer; Rigel Mahmood; Ethan Rathbun; Jinbo Bi; Kaleel Mahmood; Derek Aguiar (CoRR, 2024)

Adversarial Inception for Bounded Backdoor Poisoning in Deep Reinforcement Learning. - Ethan Rathbun; Christopher Amato; Alina Oprea (CoRR, 2024)

Hierarchical Multi-agent Reinforcement Learning for Cyber Network Defense. - Aditya Vikram Singh; Ethan Rathbun; Emma Graham; Lisa Oakley; Simona Boboila; Peter Chin 001; Alina Oprea (AAMAS, 2025)

Busting the Paper Ballot: Voting Meets Adversarial Machine Learning. - Kaleel Mahmood; Caleb Manicke; Ethan Rathbun; Aayushi Verma; Sohaib Ahmad; Nicholas Stamatakis; Laurent Michel; Benjamin Fuller 001 (CoRR, 2025)