| Back in black: A comparative evaluation of recent state-of-the-art black-box attacks K Mahmood, R Mahmood, E Rathbun, M van Dijk IEEE Access 10, 998-1019, 2021 | 27 | 2021 |
| Securing the spike: On the transferabilty and security of spiking neural networks to adversarial examples N Xu, K Mahmood, H Fang, E Rathbun, C Ding, W Wen arXiv preprint arXiv:2209.03358, 2022 | 10 | 2022 |
| Game Theoretic Mixed Experts for Combinational Adversarial Machine Learning E Rathbun, K Mahmood, S Ahmad, C Ding, M Van Dijk arXiv preprint arXiv:2211.14669, 2022 | 3 | 2022 |
| Sleepernets: Universal backdoor poisoning attacks against reinforcement learning agents E Rathbun, C Amato, A Oprea Advances in Neural Information Processing Systems 37, 111994-112024, 2025 | 1 | 2025 |
| Hierarchical Multi-agent Reinforcement Learning for Cyber Network Defense AV Singh, E Rathbun, E Graham, L Oakley, S Boboila, A Oprea, P Chin arXiv preprint arXiv:2410.17351, 2024 | 1 | 2024 |
| Adversarial Inception for Bounded Backdoor Poisoning in Deep Reinforcement Learning E Rathbun, C Amato, A Oprea arXiv preprint arXiv:2410.13995, 2024 | | 2024 |
| Hierarchical Multi-agent Reinforcement Learning for Cyber Network Defense A Vikram Singh, E Rathbun, E Graham, L Oakley, S Boboila, A Oprea, ... arXiv e-prints, arXiv: 2410.17351, 2024 | | 2024 |
| Distilling Adversarial Robustness Using Heterogeneous Teachers J Deng, A Palmer, R Mahmood, E Rathbun, J Bi, K Mahmood, D Aguiar arXiv preprint arXiv:2402.15586, 2024 | | 2024 |
| Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples N Xu, K Mahmood, H Fang, E Rathbun, C Ding, W Wen arXiv preprint arXiv:2209.03358, 2022 | | 2022 |
Kaleel MahmoodAssistant Professor, University of Rhode Islandยืนยันอีเมลแล้วที่ uri.edu